[1]

L. F. Abbott, E. T. Rolls, and M. J. Tovee. Representational capacity of face coding in monkeys. Cerebral Cortex, 6:498-505, 1996.

[2]

A. Abbott and N. Ahuja. Surface reconstruction by dynamic integration of focus, camera vergence and stereo. In Proceedings of the 2nd International Conference on Computer Vision, pages 532-545, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[3]

M. Abeles. Role of cortical neuron: integrator or coincidence detector? Israel J. Med. Sci., 18:83-92, 1982.

[4]

Y. S. Abu-Mostafa and D. Psaltis. Optical neural computing. Scientific American, 256:66-73, 1987.

[5]

E. H. Adelson. Rigid objects that appear highly non-rigid. Invest. Ophthalmol. Vis. Sci. Suppl., 26:56, 1985.

[6]

Y. Adini, Y. Moses, and S. Ullman. Face recognition: the problem of compensating for changes in illumination direction. CS-TR 93-21, Weizmann Institute of Science, 1993.

[7]

Y. Adini, Y. Moses, , and S. Ullman. Face recognition: the problem of compensating for illumination changes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997. in press.

[8]

D. W. Aha, D. Kibler, and M. A. Albert. Instance-based learning algorithms. Machine Learning, 6:37-66, 1991.

[9]

M. Ahissar and S. Hochstein. Task difficulty and the specificity of perceptual learning. Nature, 387:401-406, 1997.

[10]

V. Ajjanagadde and L. Shastri. Rules and variables in neural nets. Neural Computation, 3:121-134, 1991.

[11]

A. Albert. Regression and the Moore-Penrose pseudoinverse. Academic Press, New York, 1972.

[12]

T. D. Albright. Motion perception and the mind-body problem. Current Biology, 1:391-393, 1991.

[13]

J. Y. Aloimonos, I. Weiss, and A. Bandopadhay. Active vision. International Journal of Computer Vision, 2:333-356, 1988.

[14]

J. Y. Aloimonos and D. Shulman. Integration of visual modules: an extension of the Marr paradigm. Academic Press, Boston, 1989.

[15]

J. Y. Aloimonos and M. J. Swain. Shape from texture. In Proceedings IJCAI, pages 926-931, 1985.

[16]

J. Y. Aloimonos. Unification and integration of visual modules. In Proceedings Image Understanding Workshop, pages 507-551, San Mateo, CA, 1989. Morgan Kaufmann.

[17]

J. Y. Aloimonos. Purposive and qualitative vision. In Proc. AAAI-90 Workshop on Qualitative Vision, pages 1-5, San Mateo, CA, 1990. Morgan Kaufmann.

[18]

I. Alter and E. L. Schwartz. Psychophysical studies of shape with Fourier descriptor stimuli. Perception, 17:191-202, 1988.

[19]

R. A. Altes. Ubiquity of hyperacuity. J. Acoust. Soc. Am., 85:943-952, 1988.

[20]

S. Amari and M. Maruyama. A theory on the determination of 3D motion and 3D structure from features. Spatial Vision, 2:151-168, 1987.

[21]

S. Amari. Invariant structures of signal and feature spaces in pattern recognition problems. RAAG Memoirs, 4:553-566, 1968.

[22]

S. Amari. Feature spaces which admit and detect invariant signal transformations. In Proc. 4th Intl. Conf. Pattern Recognition, pages 452-456, Tokyo, 1978.

[23]

J. Ambros-Ingerson, R. Granger, and G. Lynch. Simulation of paleocortex performs hierarchical clustering. Science, 247:1344-1348, March 1990.

[24]

A. Ames. Visual perception and the rotating trapezoid window. Psychological Monographs, 65(7), 1951.

[25]

R. A. Andersen, R. M. Bracewell, S. Barash, J. W. Gnadt, and L. Fogassi. Eye position effects on visual, memory, and saccade-related activity in areas LIP and 7a of Macaque. J. Neurosci, 10:1176-1196, 1990.

[26]

C. H. Anderson and D. C. Van Essen. Shifter circuits: a computational strategy for dynamic aspects of visual processing. Proceedings of the National Academy of Science, 84:6297-6301, 1987.

[27]

C. Ankrum and J. Palmer. Memory for objects and parts. Perception and Psychophysics, 50:141-156, 1991.

[28]

M. A. Arbib. Feature detectors, visuomotor coordination, and efferent control. In D. Albrecht, editor, Recognition of Pattern and Form, volume 44, pages 100-110. Springer, Berlin, 1979.

[29]

F. Gregory Ashby and Nancy A. Perrin. Toward a unified theory of similarity and recognition. Psychological Review, 95(1):124-150, 1988.

[30]

F. G. Ashby, editor. Multidimensional models of perception and cognition. Erlbaum, Hillsdale, NJ, 1992.

[31]

J. J. Atick, P. A. Griffin, and A. N. Redlich. Statistical approach to shape from shading: reconstruction of three-dimensional face surfaces from single two-dimensional images. Neural Computation, 8:1321-1340, 1996.

[32]

J. J. Atick, P. A. Griffin, and A. N. Redlich. The vocabulary of shape: principal shapes for probing perception and neural response. Network, 7:1-5, 1996.

[33]

J. J. Atick and A. N. Redlich. What does the retina know about natural scenes? Neural Computation, 4:196-210, 1992.

[34]

J. J. Atick. Could information theory provide an ecological theory of sensory processing? Network, 3:213-251, 1992.

[35]

C. Atkeson and J. Hollerbach. Kinematic features of unrestrained vertical arm movements. J. Neurosci., 5:2318-2330, 1985.

[36]

R. C. Atkinson and R. M. Shiffrin. Human memory: a proposed system and its control processes. In K. W. Spence, editor, Psychology of learning and motivation: advances in research and theory, volume 2, pages 89-195. Academic Press, New York, 1968.

[37]

F. Attneave. Triangles as ambiguous figures. Am. J. Physiol., 81:447-453, ??

[38]

L. Auslander and R. E. MacKenzie. Introduction to differentiable manifolds. McGraw-Hill, 1963.

[39]

N. Ayache and O. D. Faugeras. Hyper: a new approach for the recognition and positioning of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(1):44-54, 1986.

[40]

R. Bajcsy and L. Lieberman. Texture gradient as a depth cue. Computer Graphics and Image Processing, 5:52-67, 1976.

[41]

R. Bajcsy. Active perception. Proc. IEEE, 76(8):996-1005, August 1988. special issue on Computer Vision.

[42]

P. Baldi and Y. Chauvin. Neural networks for fingerprint recognition. Neural Computation, 5:402-418, 1993.

[43]

P. Baldi and K. Hornik. Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 2:53-58, 1989.

[44]

K. Ball and R. Sekuler. Direction-specific improvement in motion discrimination. Vision Research, 27:953-965, 1987.

[45]

D. H. Ballard, R. C. Nelson, and B. Yamauchi. Animate vision. Optic News, 15:9-25, 1989.

[46]

D. H. Ballard and C. M. Brown. Computer Vision. Prentice-Hall, Englewood Cliffs, NJ, 1982.

[47]

D. H. Ballard and L. Hartman. Task frames: primitives for sensory-motor coordination. Computer Vision, Graphics, and Image Processing, 36:274-297, 1986.

[48]

D. H. Ballard and A. Ozcandarli. Eye fixation and early vision: kinetic depth. In Proceedings of the 2nd International Conference on Computer Vision, pages 524-531, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[49]

D. H. Ballard. Cortical connections and parallel processing: structure and function. Behavioral and Brain Sciences, 9:67-120, 1986.

[50]

D. H. Ballard. Interpolation coding: a representation for numbers in neural models. Biological Cybernetics, 57:389-402, 1987.

[51]

D. H. Ballard. Animate vision. Artificial Intelligence, 48:57-86, 1991.

[52]

S. Barash, R. M. Bracewell, L. Fogassi, J. W. Gnadt, and R. A. Andersen. Saccade-related activity in the lateral intraparietal area ii. Spatial properties. J. Neurophysiology, 66:1109-1124, 1991.

[53]

M. Barchilon Ben-Av, D. Sagi, and J. Braun. Visual attention and perceptual grouping. Perception and Psychophysics, 52:277-294, 1992.

[54]

H. B. Barlow and R. W. Levick. The mechanism of directional selectivity in the rabbit's retina. J. Physiol., 173:477-504, 1965.

[55]

H. B. Barlow and J. D. Mollon, editors. The senses. Cambridge University Press, Cambridge, UK, 1982.

[56]

H. B. Barlow and B. C. Reeves. The versatility and absolute efficiency of detecting mirror symmetry in random dot displays. Vision Research, 19:783-793, 1979.

[57]

H. B. Barlow. Sensory mechanisms, the reduction of redundancy, and intelligence. In The mechanisation of thought processes, pages 535-539. H.M.S.O., London, 1959.

[58]

H. B. Barlow. Single units and sensation: a neuron doctrine for perceptual psychology. Perception, 1:371-394, 1972.

[59]

H. B. Barlow. The past, present and future of feature detectors. In D. Albrecht, editor, Recognition of Pattern and Form, volume 44 of Lecture Notes in Biomathematics, pages 4-32. Springer, Berlin, 1979.

[60]

H. B. Barlow. Reconstructing the visual image in space and time. Nature, 279:189-190, 1979.

[61]

H. B. Barlow. The absolute efficiency of perceptual decisions. Proceedings of the Royal Society of London B, 290:71-82, 1980.

[62]

H. B. Barlow. Cerebral cortex as model builder. In D. Rose and V. G. Dobson, editors, Models of the visual cortex, pages 37-46. Wiley, New York, 1985.

[63]

H. B. Barlow. The role of single neurons in the psychology of perception. Quart. J. Exp. Psychol., 37A:121-145, 1985.

[64]

H. B. Barlow. Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception. Vision Research, 30:1561-1571, 1990.

[65]

H. B. Barlow. What is the computational goal of the neocortex? In C. Koch and J. L. Davis, editors, Large-scale neuronal theories of the brain, chapter 1, pages 1-22. MIT Press, Cambridge, MA, 1994.

[66]

H. B. Barlow. Adaptation by hyperpolarization. Science, 276:913-914, 1997.

[67]

S. T. Barnard and M. A. Fischler. Computational stereo. ACM Comput. Surveys, 143:553-572, 1982.

[68]

H. G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. In A. R. Hanson and E. M. Riseman, editors, Computer Vision Systems, pages 3-26. Academic Press, New York, NY, 1978.

[69]

H. G. Barrow and J. M. Tenenbaum. Computational vision. Proc. IEEE, 69:572-595, 1981.

[70]

H. G. Barrow and J. M. Tenenbaum. Interpreting line drawings as three-dimensional surfaces. Artificial Intelligence, 17:75-116, 1981.

[71]

H. G. Barrow and J. M. Tenenbaum. Retrospective on ``Interpreting line drawings as three-dimensional surfaces''. Artificial Intelligence, 59:71-80, 1993.

[72]

L. W. Barsalou. The instability of graded structure: implications for the nature of concepts. In U. Neisser, editor, Concepts and conceptual development, pages 101-140. Cambridge Univ. Press, 1987.

[73]

F. C. Bartlett. Remembering: An Experimental and Social Study. Cambridge University Press, Cambridge, 1932.

[74]

A. Barto. From chemotaxis to cooperativity: abstract exercises in neuronal learning strategies. In R. Durbin, C. Miall, and G. Mitchison, editors, The computing neuron, pages 73-98. Addison Wesley, New York, NY, 1989.

[75]

R. Basri and D. W. Jacobs. Recognition using region correspondences. International Journal of Computer Vision, 21, 1997. in press.

[76]

R. Basri and E. Rivlin. Localization and homing using combinations of model views. Artificial Intelligence, 1995. in press.

[77]

R. Basri and S. Ullman. The alignment of objects with smooth surfaces. In Proceedings of the 2nd International Conference on Computer Vision, pages 482-488, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[78]

R. Basri and S. Ullman. Recognition by linear combinations of models. Technical report, The Weizmann Institute of Science, 1989.

[79]

R. Basri. Recognition by prototypes. A.I. Memo No. 1391, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, December 1992. Intl. Journal of Computer Vision, in press.

[80]

R. Basri. Recognition by prototypes. International Journal of Computer Vision, 19(147-168), 1996.

[81]

E. B. Baum, J. Moody, and F. Wilczek. Internal representations for associative memory. Biological Cybernetics, 59:217-228, 1988.

[82]

J. Baxter. The canonical metric for vector quantization. NeuroCOLT NC-TR-95-047, University of London, 1995.

[83]

J. Baxter. Learning internal representations. In Proc. COLT'95, June 1995.

[84]

J. Baxter. The canonical distortion measure for vector quantization and approximation. Unpublished manuscript, 1996.

[85]

R. Beals, D. H. Krantz, and A. Tversky. The foundations of multidimensional scaling. Psychological Review, 75:127-142, 1968.

[86]

J. Beck, K. Prazdny, and A. Rosenfeld. A theory of textural segmentation. In J. Beck, B. Hope, and A. Rosenfeld, editors, Human and Machine Vision, pages 1-38. Academic Press, New York, NY, 1983.

[87]

J. Beck, A. Sutter, and R. Ivry. Spatial frequency channels and perceptual grouping in texture segmentation. Computer Vision, Graphics, and Image Processing, 37:299-325, 1987.

[88]

J. Beck and S. Prazdny. Highlights and the perception of glossiness. Perception and Psychophysics, 30:407-410, 1981.

[89]

J. Beck. Surface Color Perception. Cornell University Press, Ithaca, NY, 1972.

[90]

J. Beck. Textural segmentation. In J. Beck, editor, Organization and representation in perception, chapter 15. Erlbaum, Hillsdale, NJ, 1982.

[91]

J. D. Becker and J. Krüger. Recognition of visual stimuli from multiple neuronal activity in monkey visual cortex. Biological Cybernetics, 74:287-298, 1996.

[92]

A. Beinglass and H. Wolfson. Articulated object recognition, or, how to generalize the Generalized Hough Transform. Institute of Computer Sciences TR 194/90, Tel Aviv University, 1990.

[93]

R. E. Bellman. Adaptive Control Processes. Princeton University Press, Princeton, NJ, 1961.

[94]

J. Ben-Arie. The probabilistic peaking effect of viewed angles and distances with application to 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:760-774, 1990.

[95]

A. Ben-Israel and T. N. E. Greville. Generalized inverses: theory and applications. Wiley, New York, 1974.

[96]

R. G. Bennett and G. Westheimer. The effect of training on visual alignment discrimination and grating resolution. Perception and Psychophysics, 49:541-546, 1991.

[97]

S. Bentin and L. B. Feldman. The contribution of morphological and semantic relatedness to repetition priming at short and long time lags: Evidence from hebrew. Q. Journal Exp. Psychol., 42A:693-711, 1990.

[98]

R. Bergevin and M. D. Levine. Part decomposition of objects from single view line drawings. Computer Vision, Graphics, and Image Processing: Image Understanding, 55:73-83, 1992.

[99]

G. Berkeley. A treatise concerning the principles of human knowledge. Oxford University Press, Oxford, 1710/1996.

[100]

G. S. Berns, J. D. Cohen, and M. A. Mintun. Brain regions responsive to novelty in the absence of awareness. Science, 276:1272-1276, 1997.

[101]

D. C. Berry. Implicit learning: twenty-five years on. a tutorial. In C. Umiltá and M. Moscovitch, editors, Attention and Performance, volume XV, chapter 30, pages 755-781. MIT Press, 1994.

[102]

M. Bertero, T. Poggio, and V. Torre. Ill-posed problems in early vision. Proceedings of the IEEE, 76:869-889, 1988.

[103]

P. J. Besl and R. C. Jain. Invariant surface characteristics for 3D object recognition in range images. Computer Vision, Graphics, and Image Processing, 33:33-80, 1986.

[104]

D. Beymer, A. Shashua, and T. Poggio. Example based image analysis and synthesis. A.I. Memo No. 1431, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, November 1993.

[105]

D. Beymer and T. Poggio. Image representations for visual learning. Science, 272:1905-1909, 1996.

[106]

J. C. Bezdek. Fuzzy models for pattern recognition. IEEE Press, Washington, DC, 1992.

[107]

W. Bialek, F. Rieke, R. R. de Ruyter Van Steveninck, and D. Warland. Reading a neural code. Science, 252:1854-1857, 1991.

[108]

M. Bichsel and A. Pentland. Human face recognition and the face image set's topology. Computer Vision, Graphics, and Image Processing: Image Understanding, 59:254-261, 1994.

[109]

I. Biederman, J. C. Rabinowitz, A. L. Glass, and E. W. Stacy. On the information extracted from a glance at a scene. Journal of Exp. Psychol, 103:597-600, 1974.

[110]

I. Biederman, R. J. Mezzanotte, and J. C. Rabinowitz. Scene perception: Detecting and judging objects undergoing relational violations. Cognitive Psychology, 14:143-177, 1982.

[111]

I. Biederman and E. E. Cooper. Evidence for complete translational and reflectional invariance in visual object priming. Perception, 20:585-593, 1991.

[112]

I. Biederman and P. C. Gerhardstein. Recognizing depth-rotated objects: evidence and conditions for 3D viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19:1162-1182, 1993.

[113]

I. Biederman and P. C. Gerhardstein. Viewpoint-dependent mechanisms in visual object recognition: Reply to Tarr and Bülthoff. Journal of Experimental Psychology: Human Perception and Performance, 21:1506-1514, 1995.

[114]

I. Biederman and G. Ju. Surface versus edge-based determinants of visual recognition. Cognitive Psychology, 20:38-64, 1988.

[115]

I. Biederman and M. S. Shiffrar. Chicken sexing: an expert systems and experimental analysis of a difficult perceptual learning task. Journal of Experimental Psychology: Human Learning, Memory and Cognition, 13:640-645, 1987.

[116]

I. Biederman. Human image understanding: Recent research and a theory. Computer Vision, Graphics, and Image Processing, 32:29-73, 1985.

[117]

I. Biederman. Recognition by components: a theory of human image understanding. Psychol. Review, 94:115-147, 1987.

[118]

I. Biederman. Aspects and extensions of a theory of human image understanding. In Z. Pylyshyn, editor, Computational processes in human vision: an interdisciplinary perspective, pages 370-428. Ablex, Norwood, NJ, 1988.

[119]

E. Bienenstock, L. Cooper, and P. W. Munro. Theory for the development of neural selectivity: orientation specificity and binocular interaction in visual cortex. J. of Neuroscience, 2:32-48, 1982.

[120]

E. Bienenstock, S. Geman, and D. Potter. Compositionality, MDL priors, and object recognition. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Neural Information Processing Systems, volume 9. MIT Press, 1997.

[121]

E. Bienenstock and S. Geman. Compositionality in neural systems. In M. A. Arbib, editor, The handbook of brain theory and neural networks, pages 223-226. MIT Press, 1995.

[122]

E. Bienenstock. A model of neocortex. Network, 6:179-224, 1995.

[123]

J. Bigün. Recognition of local symmetries in gray value images by harmonic functions. In Proc. 9th Intl. Conf. on Patt. Recog., pages 345-347, 1988.

[124]

P. Billingsley. Probability and measure. Wiley, New York, 1979.

[125]

T. O. Binford. Visual perception by computer. In IEEE Conference on Systems and Control, Miami Beach, FL, December 1971.

[126]

T. O. Binford. Survey of model-based image analysis systems. International Journal of Robotics Research, 1:18-64, 1982.

[127]

P. O. Bishop, J. S. Coombs, and G. H. Henry. Receptive fields of simple cells in the cat striate cortex. J. Physiol. (London), 231:31-60, 1973.

[128]

S. J. Blackmore, G. Brelstaff, K. Nelson, and T. Troscianko. Is the richness of our visual world an illusion? Transsaccadic memory for complex scenes. Perception, 24:1075-1081, 1995.

[129]

A. Blake and G. Brelstaff. Geometry from specularities. In Proceedings of the 2nd International Conference on Computer Vision, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[130]

A. Blake and H. H. Bülthoff. Does the brain know the physics of specular reflection? Nature, 343:165-168, 1990.

[131]

A. Blake and A. Zisserman. Visual reconstruction. MIT Press, Cambridge, MA, 1988.

[132]

A. Blake. Specular stereo. In Proceedings IJCAI, pages 973-976, 1985.

[133]

J. F. Blinn. Models of light reflection for computer-synthesized pictures. In W. Richards, editor, Natural computation, pages 214-223. MIT Press, Cambridge, MA, 1988.

[134]

J. L. Blue, G. T. Candela, P. J. Grother, R. Chellappa, and C.L. Wilson. Evaluation of pattern classifiers for fingerprint and OCR applications. Pattern Recognition, 27:485-501, 1994.

[135]

L. M. Blumenthal. Theory and applications of distance geometry. Clarendon Press, Oxford, 1953.

[136]

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth. Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension. In 18th annual ACM symposium on theory of computing, pages 273-282, 1986.

[137]

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth. Occam's razor. Information Processing Letters, (24):377-380, 1987.

[138]

A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36:929-965, 1989.

[139]

A. Bobick and R. Bolles. The representation space paradigm of concurrent evolving object descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:146-156, 1992.

[140]

K. R. Boff, L. Kaufman, and J. P. Thomas, editors. Handbook of perception and human performance. Wiley, New York, 1986.

[141]

R. C. Bolles, P. Horaud, and M. J. Hannah. 3DPO: A three-dimensional part orientation system. In Proceedings IJCAI, pages 1116-1120, 1983.

[142]

R. C. Bolles, H. H. Baker, and M. J. Hannah. The JISCT stereo evaluation. In ARPA Image Understanding Workshop, pages 263-274, 1993.

[143]

R. C. Bolles and R. A. Cain. Recognizing and locating partially visible objects: the local feature focus method. International Journal of Robotics Research, 1:57-82, 1982.

[144]

R. C. Bolles and P. Horaud. 3DPO: A three-dimensional part orientation system. International Journal of Robotics Research, 5:3-26, 1986.

[145]

F. L. Bookstein. Morphometric tools for landmark data: geometry and biology. Cambridge Univ. Press, New York, 1991.

[146]

F. L. Bookstein. Biometrics, biomathematics and the morphometric synthesis. Bulletin of Mathematical Biology, 58:313-365, 1996.

[147]

I. Borg and J. Lingoes. Multidimensional Similarity Structure Analysis. Springer, Berlin, 1987.

[148]

S. M. Botros and C. G. Atkeson. Generalization properties of radial basis functions. In D. Touretzky, editor, Neural Information Processing Systems, volume 3, pages 707-713, San Mateo, CA, 1991. Morgan Kaufmann.

[149]

J. Bourgain. On Lipschitz embedding of finite metric spaces in Hilbert space. Israel J. Math., 52:46-52, 1985.

[150]

R. M. Boynton. Color, hue, and wavelength. In E. C. Carterette and M. P. Friedman, editors, Handbook of Perception, volume V, pages 301-347. Academic Press, New York, NY, 1978.

[151]

R. M. Boynton. Color in contour and object perception. In E. C. Carterette and M. P. Friedman, editors, Handbook of Perception, volume VIII, pages 173-199. Academic Press, New York, NY, 1978.

[152]

O. J. Braddick. Low-level and high-level processes in apparent motion. Phil. Trans. R. Soc. London B, 290:137-151, 1980.

[153]

M. Brady and A. Yuille. An extremum principle for shape from contour. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:288-301, 1984.

[154]

M. J. Brady. Computational approaches to image understanding. ACM Computing Surveys, 14:3-71, 1982.

[155]

M. J. Brady. Criteria for representations of shape. In J. Beck, B. Hope, and A. Rosenfeld, editors, Human and machine vision, pages 39-84. Academic Press, New York, 1983.

[156]

V. Braitenberg. On the texture of brains. Springer-Verlag, New York, 1977.

[157]

J. Braun and D. Sagi. Texture-based tasks are little affected by second tasks requiring peripheral or central attentive fixation. Perception, 20:483-500, 1991.

[158]

M. Bravo and R. Blake. Preattentive vision and perceptual groups. Perception, 19:515-522, 1990.

[159]

C. Bregler and S. M. Omohundro. Nonlinear image interpolation using manifold learning. In D. S. Touretzky G. Tesauro and T. K. Leen, editors, Advances in Neural Information Processing 7, pages 973-980. MIT Press, 1995.

[160]

T. M. Breuel. Adaptive model base indexing. A.I. Memo No. 1008, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1989.

[161]

E. Bricolo, T. Poggio, and E. Logothetis. 3D object recognition: a model of view-tuned units. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8. MIT Press, Cambridge, MA, 1996.

[162]

E. Bricolo and H. H. Bülthoff. Translation-invariant features for object recognition. Perception, 21 (supp.2):59, 1992.

[163]

E. Bricolo and H. H. Bülthoff. Further evidence for viewer-centered representations. Perception, 22 (supp):105, 1993.

[164]

E. Bricolo and H. H. Bülthoff. Rotation, translation, size and illumination invariances in 3D object recognition. Invest. Ophthalm. Vis. Science, 34(4):1081, 1993.

[165]

J. C. Brigham. The influence of race on face recognition. In H. D. Ellis, M. A. Jeeves, and F. Newcombe, editors, Aspects of face processing, pages 170-177. Martinus Nijhoff, Dordrecht, 1986.

[166]

R. A. Brooks. Symbolic reasoning among 3D models and 2D images. Artificial Intelligence, 17:285-348, 1981.

[167]

R. Brooks. Model-based three-dimensional interpretations of two-dimensional images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5:140-149, 1983.

[168]

L. R. Brooks. Decentralized control of categorization: the role of prior processing episodes. In U. Neisser, editor, Concepts and conceptual development, pages 141-174. Cambridge Univ. Press, 1987.

[169]

R. Brooks. Intelligence without representation. Artificial Intelligence, 47:139-160, 1991.

[170]

R. Brooks. Intelligence without representation. In Proc. of Foundations of AI Workshop, MIT, June 1987.

[171]

D. S. Broomhead and D. Lowe. Multivariable functional interpolation and adaptive networks. Complex Systems, 2:321-355, 1988.

[172]

T. H. Brown, E. W. Kairiss, and C. L. Keenan. Hebbian synapses: biophysical mechanisms and algorithms. Ann. Rev. Neurosci., 13:475-511, 1990.

[173]

J. M. Brown, N. Weisstein, and J. G. May. Visual search for simple volumetric shapes. Perception and Psychophysics, 51:40-48, 1992.

[174]

D. R. Brown and M. H. Andrews. Visual form discrimination: multidimensional analysis. Perception and Psychophysics, 3:401-406, 1968.

[175]

V. Bruce, P. Healey, M. Burton, T. Doyle, A. Coombes, and A. Linney. Recognising facial surfaces. Perception, 20:755-770, 1991.

[176]

V. Bruce, E. Hanna, N. Dench, P. Healy, and M. Burton. The importance of `mass' in line drawings of faces. Applied Cognitive Psychology, 6:619-628, 1992.

[177]

V. Bruce, M. Burton, , E. Hanna, P. Healey, O. Mason, A. Coombes, R. Fright, and A. Linney. Sex discrimination: how do we tell the difference between male and female faces? Perception, 22:131-152, 1993.

[178]

V. Bruce, A. M. Burton, and N. Dench. What's distinctive about a distinctive face? Quarterly Journal of Experimental Psychology, 47A:119-141, 1994.

[179]

R. Brunelli and T. Poggio. HyperBF networks for real object recognition. In Proceedings IJCAI, pages 1278-1284, Sydney, Australia, 1991.

[180]

R. Brunelli and T. Poggio. Face recognition through geometrical features. In G. Sandini, editor, Proc. 2nd European Conf. on Computer Vision, Lecture Notes in Computer Science, volume 588, pages 792-800. Springer Verlag, 1992.

[181]

E. Brunswik. Perception and the representative design of psychological experiments. U. of California Press, Berkeley, CA, 1956.

[182]

A. Bruss and B. K. P. Horn. Passive navigation. Computer Vision, Graphics, and Image Processing, 21:3-20, 1983.

[183]

G. Buchsbaum and A. Gottschalk. Chromaticity coordinates of frequency-limited functions. Journal of the Optical Society of America, 1:885-887, 1984.

[184]

H. H. Bülthoff, K. G. Götz, and M. Herre. Recurrent inversion of visual orientation in the walking fly, drosophila melanogaster. J. Comp. Physiol., 148:471-481, 1982.

[185]

H. H. Bülthoff, J. J. Little, and T. Poggio. A parallel algorithm for real-time computation of motion. Nature, 337:549-553, 1989.

[186]

H. H. Bülthoff, S. Edelman, and E. Sklar. Mapping the generalization space in object recognition. Invest. Ophthalm. Vis. Science Suppl., 32(3):996, 1991.

[187]

H. H. Bülthoff, S. Edelman, and E. Sklar. Generalizing object recognition over 2d and 3d transformations. A. I. Memo, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1992. in preparation.

[188]

H. H. Bülthoff, S. Edelman, and M. J. Tarr. How are three-dimensional objects represented in the brain? Cerebral Cortex, 5:247-260, 1995.

[189]

H. H. Bülthoff and I. Bülthoff. Combining neuropharmacology and behavior to study movement detection in flies. Biological Cybernetics, 55:313-320, 1987.

[190]

H. H. Bülthoff and S. Edelman. The role of binocular stereo cues in visual object recognition. Perception, 19:340, 1990.

[191]

H. H. Bülthoff and S. Edelman. Psychophysical support for a 2-D view interpolation theory of object recognition. Proceedings of the National Academy of Science, 89:60-64, 1992.

[192]

H. H. Bülthoff and S. Edelman. Evaluating object recognition theories by computer graphics psychophysics. In T. Poggio and D. Glaser, editors, Exploring Brain Functions: Models in Neuroscience, pages 139-164. Wiley, New York, 1993. Proc. Dahlem Workshop.

[193]

H. H. Bülthoff and K. G. Götz. Analogous motion illusion in man and fly. Nature, 278:636-638, 1979.

[194]

H. H. Bülthoff and D. Kersten. Interactions between transparency and depth. Perception, 18:A22b, 1989.

[195]

H. H. Bülthoff and H. A. Mallot. Interaction of different modules in depth perception. In Proceedings of the 1st International Conference on Computer Vision, pages 295-305, June 1987.

[196]

H. H. Bülthoff and H. A. Mallot. Interaction of depth modules: stereo and shading. Journal of the Optical Society of America, 5:1749-1758, 1988.

[197]

H. H. Bülthoff and H. A. Mallot. Integration of stereo, shading and texture. In A. Blake and T. Troscianko, editors, AI and the Eye. Wiley, London, UK, 1990.

[198]

H. H. Bülthoff. Figure-ground discrimination in the visual system of drosophila melanogaster. Biological Cybernetics, 41:139-145, 1981.

[199]

H. H. Bülthoff. Drosophila mutants disturbed in visual orientation. ii. mutants affected in movement and position computation. Biological Cybernetics, 45:71-77, 1982.

[200]

H. H. Bülthoff. Shape from X: Stereo, texture, specularity. In M. Landy and A. Movshon, editors, Computational Models of Visual Processing. MIT Press, Cambridge, MA, 1991.

[201]

J. B. Burns, R. Weiss, and E. Riseman. View variation of point-set and line segment features. In Proceedings Image Understanding Workshop, pages 650-659, April 1990.

[202]

J. B. Burns, R. Weiss, and E. Riseman. View variation of point-set and line segment features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15:51-68, 1993.

[203]

D. J. Burr. Matching elastic templates. In O. J. Braddick and A. C. Sleigh, editors, Physical and biological processing of images, pages 260-270. Springer-Verlag, Berlin, 1983.

[204]

P. Burt. Smart sensing within a Pyramid Vision Machine. Proc. IEEE, 76:139-153, 1988.

[205]

M. Burton, V. Bruce, and N. Dench. What's the difference between men and women? evidence from facial measurement. Perception, 22:153-176, 1993.

[206]

T. A. Busey, N. P. Brady, and J. E. Cutting. Compensation is unnecessary for the perception of faces in slanted pictures. Perception and Psychophysics, 48:1-11, 1990.

[207]

A. Califano and R. Mohan. Multidimensional indexing for recognizing visual shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:373-392, 1994.

[208]

F. W. Campbell and J. G. Robson. Application of Fourier analysis to the visibility of gratings. J. Physiol. (Lond.), 197:551-566, 1968.

[209]

D. T. Campbell. Pattern matching as an essential in distal knowing. In H. Kornblith, editor, Naturalizing epistemology, pages 49-70. MIT Press, 1985.

[210]

J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:679-698, 1986.

[211]

B. Caprile, F. Girosi, and T. Poggio, 1991. in preparation.

[212]

B. Caprile and F. Girosi. A non-deterministic minimization algorithm. A. I. Memo 1254, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, Sept. 1990.

[213]

M. Carandini and D. Ferster. A tonic hyperpolarization underlying contrast adaptation in cat visual cortex. Science, 276:949-952, 1997.

[214]

M Carandini and D J Heeger. Summation and division by neurons in primate visual cortex. Science, 264:1333-1336, 1994.

[215]

S. Carey, R. Diamond, and B. Woods. Development of face recognition-a maturational component? Developmental Psychology, 16:257-269, 1980.

[216]

S. Carey and R. Diamond. From piecemeal to configurational representation of faces. Science, 195:312-314, 1977.

[217]

C. R. Carlson and R. W. Klopfenstein. Spatial frequency model for hyperacuity. Journal of the Optical Society of America, A2:1747-1751, 1985.

[218]

E. H. Carlton. Connection between internal representation of rigid transformation and cortical activity paths. Biological Cybernetics, 59:419-429, 1988.

[219]

G. J. Carman and L. Welch. Three-dimensional illusory contours and surfaces. Nature, 360:585-587, 1992.

[220]

T. K. Carne. The geometry of shape spaces. Proc. Lond. Math. Soc., 61:407-432, 1990.

[221]

G. A. Carpenter, S. Grossberg, and J. Reynolds. ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. CAS/CNS-TR 91-001, Boston University, 1991.

[222]

G. A. Carpenter, S. Grossberg, and D. B. Rosen. Fuzzy ART: An adaptive resonance algorithm for rapid stable classification of analog patterns. In Proc. Intl. Joint Conf. on Neural Networks, pages 411-416, 1991.

[223]

G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. on Neural Networks, 3:698-713, 1992.

[224]

G. A. Carpenter and S. Grossberg. Adaptive resonance theory: Neural network architectures for self-organizing pattern recognition. In R. Eckmiller, G. Hartmann, and G. Hauske, editors, Parallel Processing in Neural Systems and Computers, pages 383-389. North-Holland, Amsterdam, 1990.

[225]

J. D. Carroll and J. J. Chang. Analysis of individual differences in multidimensional scaling via an N-way generalization of the Eckart-Young decomposition. Psychometrika, 35:283-319, 1970.

[226]

E. C. Carterette and M. P. Friedman. Psychophysical judgment and measurement. vol. 2 in the Handbook of Perception, 1978.

[227]

P. Cavanagh. Local log polar frequency analysis in the striate cortex as a basis for size and orientation invariance. In D. Rose and V. G. Dobson, editors, Models of the visual cortex, pages 146-157. Wiley, New York, NY, 1985.

[228]

P. Cavanagh. Reconstructing the third dimension: interactions between color, texture, motion, binocular disparity and shape. Computer Vision, Graphics, and Image Processing, 37:171-195, 1987.

[229]

P. Cavanagh. Vision is getting easier every day. Perception, 24:1227-1232, 1995. guest editorial.

[230]

J. Cerella. Pigeons and perceptrons. Pattern Recognition, 19:431-438, 1987.

[231]

J. Cerella. Pigeon pattern perception: limits on perspective invariance. Perception, 19:141-159, 1990.

[232]

D. Chalmers. Absent qualia, fading qualia, dancing qualia. In T. Metzinger, editor, Conscious experience. Imprint Academic, 1995.

[233]

E. C. Charles and N. K. Logothetis. The responses of middle temporal (mt) neurons to isoluminant stimuli. Invest. Ophthalm. Vis. Science, 30:427, 1989.

[234]

R. Chellappa, R. Chatterjee, and R. Baghdazian. Texture synthesis and coding using Gaussian Markov field models. IEEE Trans. SMC, 15:298-303, 1985.

[235]

S. Chen and D. L. Donoho. Basis pursuit. In Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers, volume 1, pages 41-44, Pacific Grove, CA, 1994. IEEE Comput. Soc. Press.

[236]

S. S. Chen and M. Penna. Shape and motion of nonrigid bodies. Computer Vision, Graphics, and Image Processing, 36:175-207, 1986.

[237]

K. Cheng, T. Hasegawa, K. Saleem, and K. Tanaka. Comparison of neuronal selectivity for stimulus speed, length, and contrast in the prestriate visual cortical areas V4 and MT of the macaque monkey. J. of Neurophysiology, 71(6):2269-2280, 1994.

[238]

G. Chevalier, S. Vacher, J. M. Deniau, and M. Desban. Disinhibition as a basic process in the expression of striatal function. I. The striatonigral influence on tecto-spinal/tecto-diencephalic neurons. Brain Research, 334:215-226, 1985.

[239]

R. T. Chin and C. R. Dyer. Model-based recognition in robot vision. ACM Comp. Surv., 18:67-108, 1986.

[240]

P. S. Churchland. Neurophilosophy. MIT Press, Cambridge, MA, 1987.

[241]

P. M. Churchland. A neurocomputational perspective. MIT Press, Cambridge, MA, 1989.

[242]

J. J. Clark and N. Ferrier. Modal control of an attentive visual system. In Proceedings of the 2nd International Conference on Computer Vision, pages 514-523, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[243]

A. Clark. Sensory qualities. Clarendon Press, Oxford, 1993.

[244]

W. F. Clocksin. Perception of surface slant and edge labels from optical flow: a computational approach. Perception, 9:253-269, 1980.

[245]

E.E. Clothiaux, L. N Cooper, and M.F. Bear. Synaptic plasticity in visual cortex: Comparison of theory with experiment. Journal of Physiology, 66:1785-1804, 1991.

[246]

L. Coetzee and E. C. Botha. Fingerprint recognition in low quality images. Pattern Recognition, 26:1441-1460, 1993.

[247]

J. Cohen. Dependency of the spectral reflectance curves of the Munsell color chips. Psychonomic Sciences, 1:369-370, 1964.

[248]

D. Cohn, E. Riskin, and R. Ladner. Theory and practice of vector quantizers trained on small training sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:54-65, 1994.

[249]

H. Cohn. Conformal mappings on Riemann surfaces. McGraw-Hill, New York, 1967.

[250]

Neural networks. special issue of IEEE Computer, March 1988.

[251]

J. H. Connell. Learning shape descriptions: generating and generalizing models of visual objects. A.I. TR No. 853, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1985.

[252]

L.A. Cooper. Demonstration of a mental analog of an external rotation. Perception and Psychophysics, 19:296-302, 1976.

[253]

C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20:273-297, 1995.

[254]

J. M. Cortese and B. P. Dyre. Perceptual similarity of shapes generated from Fourier Descriptors. Journal of Experimental Psychology: Human Perception and Performance, 22:133-143, 1996.

[255]

S. Cost and S. Salzberg. A weighted nearest-neighbor algorithm for learning with symbolic features. Machine Learning, 10:57-78, 1993.

[256]

R. M. J. Cotterill, editor. Computer simulation in brain science. Cambridge Univ. Press, Cambridge, 1988.

[257]

G. W. Cottrell, P. Munro, and D. Zipser. Learning internal representations from gray-scale images: An example of extensional programming. In Ninth Annual Conference of the Cognitive Science Society, pages 462-473, Hillsdale, 1987. Erlbaum.

[258]

T. Cover and P. Hart. Nearest neighbor pattern classification. IEEE Trans. on Information Theory, IT-13:21-27, 1967.

[259]

J. Cowie and W. Lehnert. Information extraction. Communications of the ACM, 39:80-91, 1996.

[260]

I. J. Cox, S. Hingorani, B. M. Maggs, and S. B. Rao. Stereo without disparity gradient smoothing: a Bayesian sensor fusion solution. In British Machine Vision Conf., pages 337-346, Berlin, 1992. Springer-Verlag.

[261]

F. H. C. Crick, D. C. Marr, and T. Poggio. An information-processing approach to understanding the visual cortex. In F. Schmitt, editor, The organization of the cerebral cortex. MIT Press, Cambridge, MA, 1981.

[262]

F. Crick and C. Koch. Towards a neurobiological theory of consciousness. Seminars in the Neurosciences, 2:263-275, 1990.

[263]

R. Cummins. Meaning and mental representation. MIT Press, Cambridge, MA, 1989.

[264]

R. Cummins. Representations, Targets, and Attitudes. MIT Press, Cambridge, MA, 1996.

[265]

J. E. Cutting and R. T. Millard. Three gradients and the perception of flat and curved surfaces. J. of Exp. Psychology: General, 113:198-216, 1984.

[266]

F. Cutzu and S. Edelman. Viewpoint-dependence of response time in object recognition. CS-TR 10, Weizmann Institute of Science, 1992.

[267]

F. Cutzu and S. Edelman. Canonical views in object representation and recognition. Vision Research, 34:3037-3056, 1994.

[268]

F. Cutzu and S. Edelman. Explorations of shape space. CS-TR 95-01, Weizmann Institute of Science, 1995.

[269]

F. Cutzu and S. Edelman. Faithful representation of similarities among three-dimensional shapes in human vision. Proceedings of the National Academy of Science, 93:12046-12050, 1996.

[270]

F. Cutzu and S. Edelman. Representation of object similarity in human vision: psychophysics and a computational model. Vision Research, 1997. in press.

[271]

F. Cutzu. Viewpoint effects and visual similarity in object perception as a basis for understanding visual representation. PhD thesis, Weizmann Institute of Science, 1996.

[272]

G. Cybenko. Approximations by superpositions of sigmoidal functions. Math. Control, Signals, Systems, 2:303-314, 1989.

[273]

D. Cyganski and J. A. Orr. Application of tensor theory to object recognition and orientation determination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:662-673, 1985.

[274]

A. R. Damasio. The brain binds entities and events by multiregional activation from convergence zones. Neural Computation, 1:123-132, 1989.

[275]

J. G. Daugman. Spatial visual channels in the fourier plane. Vision Research, 24:891-910, 1984.

[276]

J. G. Daugman. An information-theoretic view of analog representation in the striate cortex. In E. L. Schwartz, editor, Computational Neuroscience, pages 403-423. MIT Press, 1988.

[277]

D. Davidson. Essays on actions and events. Clarendon Press, Oxford, 1980.

[278]

P. J. Davis and P. Rabinowitz. Methods of numerical integration. Academic Press, New York, 1975.

[279]

L. S. Davis. A survey of edge detection techniques. Computer Graphics and Image Processing, 4:248-270, 1975.

[280]

S. Dawis, R. Shapley, E. Kaplan, and D. Tranchina. The receptive field organization of X-cells in the cat: spatiotemporal coupling and asymmetry. Vision Research, 24:549-564, 1984.

[281]

P. Dayan, G. E. Hinton, and R. M. Neal. The Helmholtz Machine. Neural Computation, 7:889-904, 1995.

[282]

R. L. De Valois and K. K. De Valois. Neural coding of color. In E. C. Carterette and M. P. Friedman, editors, Handbook of Perception, volume V, pages 117-166. Academic Press, New York, NY, 1978.

[283]

D. DeCarlo and D. Metaxas. Blended deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18:443-448, 1996.

[284]

P. Demartines. Analyse de données par réseaux de neurones auto-organisants. PhD thesis, Inst. National Polytechnique de Grenoble, 1994.

[285]

D. DeMers and G. Cottrell. Nonlinear dimensionality reduction. In Stephen José Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems 5, pages 580-587. Morgan Kaufmann, 1993.

[286]

D. C. Dennett. The intentional stance. MIT Press, Cambridge, MA, 1987.

[287]

D. C. Dennett. When Philosophers Encounter Artificial Intelligence. Daedalus, 117:283-295, 1988.

[288]

D. C. Dennett. Consciousness explained. Little, Brown & Company, Boston, MA, 1991.

[289]

J. B. Deregowski. Real space and represented space: cross-cultural perspectives. Behav. Brain Sciences, 12:51-119, 1989.

[290]

R. Desimone, T. D. Albright, C. G. Gross, and C. J. Bruce. Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci., 4:2051-2062, 1984.

[291]

R. Desimone, S. J. Schein, J. Moran, and L. G. Ungerleider. Contour, color and shape analysis beyond the striate cortex. Vision Research, 25:441-452, 1985.

[292]

R. Desimone and L.G. Ungerleider. Neural mechanisms of visual processing in monkeys. In F. Boler and J. Grafman, editors, Handbook of Neuropsychology, volume 2, pages 267-299. Elsevier, Amsterdam, 1989.

[293]

R. Diamond and S. Carey. Why faces are and are not special: an effect of expertise. Journal of experimental psychology, 115(2):107-117, 1986.

[294]

S. J. Dickinson, A. P. Pentland, and A. Rosenfeld. 3-D shape recovery using distributed aspect matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:174-198, 1992.

[295]

M. do Carmo. Differential geometry of curves and surfaces. Prentice-Hall, Englewood Cliffs, NJ, 1976.

[296]

P. C. Dodwell. Human perception of patterns and objects. In R. Held, H. W. Leibowitz, and H.-L. Teuber, editors, Handbook of sensory physiology: Perception, chapter 15, pages 523-548. Springer-Verlag, Berlin, 1978.

[297]

P. C. Dodwell. The Lie transformation group model of visual perception. Perception and Psychophysics, 34:1-16, 1983.

[298]

B. Dresp. Local brightness mechanisms sketch out surfaces but do not fill them in: psychophysical evidence in the kanisza square. Perception and Psychophysics, 52:562-570, 1992.

[299]

F. Dretske. Knowledge and the flow of information. MIT Press, Cambridge, MA, 1981.

[300]

F. Dretske. Seeing, believing, and knowing. In D. N. Osherson, S. M. Kosslyn, and J. M. Hollerbach, editors, Visual cognition and action, volume 2, pages 129-148. MIT Press, Cambridge, MA, 1990.

[301]

M. Drumheller and T. Poggio. On parallel stereo. In Proceedings of IEEE Conference on Robotics and Automation, 1986.

[302]

R. O. Duda and P. E. Hart. Pattern classification and scene analysis. Wiley, New York, 1973.

[303]

J. Duncan and G. W. Humphreys. Visual search and stimulus similarity. Psychol. Review, 96:433-458, 1989.

[304]

R. Durbin and G. Mitchison. A dimension reduction framework for understanding cortical maps. Nature, 343:644-647, 1990.

[305]

S. Duvdevani-Bar, F. Cutzu, and S. Edelman. A computational model of similarity-based representation of the shape space. CS-TR, Weizmann Institute of Science, 1995. in preparation.

[306]

S. Duvdevani-Bar and S. Edelman. On similarity to prototypes in 3D object representation. CS-TR 95-11, Weizmann Institute of Science, 1995.

[307]

M. D'Zmura and G. Iverson. A formal approach to color constancy: the recovery of surface and light source spectral properties using bilinear models. In C. Dowling, F. Roberts, and P. Theuns, editors, Recent Progress in Mathematical Psychology. Erlbaum, Hillsdale, NJ, 1997.

[308]

J. T. Todd E. Mingolla. Perception of solid shape from shading. Biological Cybernetics, 53:137-151, 1986.

[309]

U. Eco. Meaning and mental representations. Indiana University Press, Bloomington, IN, 1988.

[310]

S. Edelman, H. Bülthoff, and D. Weinshall. Exploring representation of 3D objects for visual recognition. In Invest. Ophthalm. Vis. Science, volume 30, page 252, 1989.

[311]

S. Edelman, H. Bülthoff, and D. Weinshall. Integrating information for visual recognition of 3D objects. Perception, 18:517, 1989.

[312]

S. Edelman, S. Ullman, and T. Flash. Reading cursive handwriting by alignment of letter prototypes. International Journal of Computer Vision, 5:303-331, 1990.

[313]

S. Edelman, D. Reisfeld, and Y. Yeshurun. A system for face recognition that learns from examples. CS-TR 91-20, Weizmann Institute of Science, October 1991. to appear in Proc. 2nd European Conf. on Computer Vision.

[314]

S. Edelman, D. Reisfeld, and Y. Yeshurun. Learning to recognize faces from examples. In G. Sandini, editor, Proc. 2nd European Conf. on Computer Vision, Lecture Notes in Computer Science, volume 588, pages 787-791. Springer Verlag, 1992.

[315]

S. Edelman, D. Weinshall, H. Bülthoff, and T. Poggio. A model of the acquisition of object representations in human 3D visual recognition. In P. Dario, G. Sandini, and P. Aebischer, editors, Proc. NATO Advanced Research Workshop on Robots and Biological Systems, pages 99-118. Springer Verlag, 1993.

[316]

S. Edelman, H. H. Bülthoff, and I. Bülthoff. Features of the representation space for 3D objects. MPIK-TR 40, Max Planck Institute for Biological Cybernetics, September 1996.

[317]

S. Edelman, H. H. Bülthoff, and I. Bülthoff. Interdependence of feature dimensions in the representation of 3D objects. Invest. Ophthalm. Vis. Sci. Suppl. (Proc. ARVO), April 1996. abstract.

[318]

S. Edelman, F. Cutzu, and S. Duvdevani-Bar. Similarity to reference shapes as a basis for shape representation. In G. W. Cottrell, editor, Proceedings of 18th Annual Conf. of the Cognitive Science Society, pages 260-265, San Diego, CA, July 1996.

[319]

S. Edelman, N. Intrator, and T. Poggio. Complex cells and object recognition, 1997. submitted.

[320]

S. Edelman, H. H. Bülthoff, and E. Sklar. Task and object learning in visual recognition. A. I. Memo 1348, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, April 1991.

[321]

S. Edelman, H. Bülthoff, and D. Weinshall. Stimulus familiarity determines recognition strategy for novel 3D objects. A.I. Memo No. 1138, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, July 1989.

[322]

S. Edelman and H. H. Bülthoff. Generalization of object recognition in human vision across stimulus transformations and deformations. In Y. Feldman and A. Bruckstein, editors, Proc. 7th Israeli AICV Conference, pages 479-487. Elsevier, 1990.

[323]

S. Edelman and H. H. Bülthoff. Viewpoint-specific representations in 3D object recognition. A.I. Memo No. 1239, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1990.

[324]

S. Edelman and H. H. Bülthoff. Modeling human visual object recognition. In Proc. IJCNN-92, volume IV, pages 37-42, 1992.

[325]

S. Edelman and H. H. Bülthoff. Orientation dependence in the recognition of familiar and novel views of 3D objects. Vision Research, 32:2385-2400, 1992.

[326]

S. Edelman and S. Duvdevani-Bar. A model of visual recognition and categorization. Phil. Trans. R. Soc. Lond. (B), 352:--, 1997. to appear.

[327]

S. Edelman and S. Duvdevani-Bar. Similarity, connectionism, and the problem of representation in vision. Neural Computation, 9:701-720, 1997.

[328]

G. M. Edelman and L. Finkel. Neuronal group selection in the cerebral cortex. In G. M. Edelman, W. E. Gall, and W. M. Cowan, editors, Dynamical aspects of neocortical function, pages 653-695. Wiley, New York, 1984.

[329]

S. Edelman and T. Flash. A model of handwriting. Biological Cybernetics, 57:25-36, 1987.

[330]

S. Edelman and N. Intrator. Learning as extraction of low-dimensional representations. In D. Medin, R. Goldstone, and P. Schyns, editors, Mechanisms of Perceptual Learning. Academic Press, 1997. in press.

[331]

S. Edelman and N. Intrator. Learning as formation of low-dimensional representation spaces. In J. Elman, editor, Proc. 19th Cognitive Science Society Meeting, Stanford, CA, 1997. in press.

[332]

S. Edelman and T. Poggio. Bringing the Grandmother back into the picture: a memory-based view of object recognition. A.I. Memo No. 1181, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1990. also in Int. J. Pattern Recog. Artif. Intell. 6:37-62, 1992.

[333]

S. Edelman and T. Poggio. Models of object recognition. Current Opinion in Neurobiology, 1:270-273, 1991.

[334]

S. Edelman and T. Poggio. Bringing the Grandmother back into the picture: a memory-based view of object recognition. Int. J. Pattern Recog. Artif. Intell., 6:37-62, 1992.

[335]

S. Edelman and T. Poggio. Integrating visual cues for object segmentation and recognition. Optic News, 15:8-15, May 1989.

[336]

S. Edelman and T. Poggio. Representations in high-level vision: reassessing the inverse optics paradigm. In Proc. DARPA Image Understanding Workshop, pages 944-949, San Mateo, CA, May 1989. Morgan Kaufman.

[337]

S. Edelman and S. Ullman. Reading cursive script by computer. In Proc. 42nd SPSE Conference, pages 179-182, Boston, MA, May 1989.

[338]

S. Edelman and D. Weinshall. Computational vision: a critical review. In R. Watt, editor, Vision and visual dysfunction, volume 14, chapter 4, pages 30-49. Macmillan, London, 1991.

[339]

S. Edelman and D. Weinshall. A self-organizing multiple-view representation of 3D objects. Biological Cybernetics, 64:209-219, 1991.

[340]

S. Edelman and D. Weinshall. Computational approaches to shape constancy. In V. Walsh and J. Kulikowski, editors, Perceptual constancies: why things look as they do. Cambridge University Press, Cambridge, UK, 1997. in press.

[341]

S. Edelman and D. Weinshall. A self-organizing multiple-view representation of 3D objects. A.I. Memo No. 1146, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, August 1989.

[342]

S. Edelman and D. Weinshall. Computational vision: a critical review. A.I. Memo No. 1158, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, October 1989.

[343]

S. Edelman. Line connectivity algorithms for an asynchronous pyramid computer. Computer Vision, Graphics, and Image Processing, 40:169-187, 1987.

[344]

S. Edelman. Reading and writing of cursive script: a computational study. PhD thesis, Weizmann Institute of Science, 1988.

[345]

S. Edelman, 1989. unpublished observations.

[346]

S. Edelman. Reading cursive handwriting. Perception, 18:524, 1989.

[347]

S. Edelman. Features of recognition. CS-TR 91-10, Weizmann Institute of Science, 1991.

[348]

S. Edelman. A network model of object recognition in human vision. In H. Wechsler, editor, Neural networks for perception, volume 1, pages 25-40. Academic Press, New York, 1991.

[349]

S. Edelman. On learning to recognize 3D objects from examples. CS-TR 91-3, Weizmann Institute of Science, 1991.

[350]

S. Edelman. Visual perception. In S. Shapiro, editor, Encyclopedia of AI, volume 2, pages 1655-1663. Wiley, New York, 1991.

[351]

S. Edelman. Class similarity and viewpoint invariance in the recognition of 3D objects. CS-TR 92-17, Weizmann Institute of Science, 1992.

[352]

S. Edelman. Representing 3D objects by sets of activities of receptive fields. CS-TR 92-19, Weizmann Institute of Science, 1992.

[353]

S. Edelman. On learning to recognize 3D objects from examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15:833-837, 1993.

[354]

S. Edelman. Representation, Similarity, and the Chorus of Prototypes. CS-TR 93-10, Weizmann Institute of Science, 1993. to appear in Minds and Machines, 1995.

[355]

S. Edelman. Representing 3D objects by sets of activities of receptive fields. Biological Cybernetics, 70:37-45, 1993.

[356]

S. Edelman. Biological constraints and the representation of structure in vision and language. Psycoloquy, 5(57), 1994.

[357]

S. Edelman. Biological constraints and the representation of structure in vision and language. Psycoloquy, 5(57), September 1994. available electronically as ftp://ftp.princeton.edu/pub/harnad/Psycoloquy/1994.volume.5/psyc.94.5.57.lan% guage-network.3.edelman.

[358]

S. Edelman. Representation without reconstruction. Computer Vision, Graphics, and Image Processing, 60:92-94, 1994.

[359]

S. Edelman. Class similarity and viewpoint invariance in the recognition of 3D objects. Biological Cybernetics, 72:207-220, 1995.

[360]

S. Edelman. Representation of similarity in 3D object discrimination. Neural Computation, 7:407-422, 1995.

[361]

S. Edelman. Representation, Similarity, and the Chorus of Prototypes. Minds and Machines, 5:45-68, 1995.

[362]

S. Edelman. Why have lateral connections in the visual cortex? In J. Sirosh, R. Miikkulainen, and Y. Choe, editors, Lateral Interactions in the Cortex: Structure and Function. electronic book, http://www.cs.utexas.edu/users/nn/lateral_interactions_book/cover.html edition, 1995.

[363]

S. Edelman. Representation is representation of similarity. CS-TR 96-08, Weizmann Institute of Science, 1996. submitted to Behavior and Brain Sciences.

[364]

S. Edelman. Receptive fields for vision: from hyperacuity to object recognition. In R. Watt, editor, Vision. MIT Press, Cambridge, MA, 1997. in press.

[365]

S. Edelman. Representation and recognition in vision. 1997. forthcoming.

[366]

S. Edelman. Representation is representation of similarity, 1997. Behavioral and Brain Sciences, to appear.

[367]

S. Edelman. Vision reanimated. In Y. Aloimonos, S. Carlsson, and J.-O. Eklundh, editors, Proc. 7th Rosenön Workshop on Computer Vision. L. Erlbaum, Hillsdale, NJ, 1997. forthcoming.

[368]

M. Eden. On the formalization of handwriting. In Proc. Symp. Appl. Math., volume 12, pages 83-88, Providence, RI, 1961. Amer. Math. Soc.

[369]

B. Efron and R. Tibshirani. An introduction to the bootstrap. Chapman and Hall, London, 1993.

[370]

P. D. Eimas and A. M. Galaburda, editors. Neurobiology of cognition. MIT Press, Cambridge, MA, 1990.

[371]

G. Ekman and R. Lindman. Multidimensional ratio scaling and multidimensional similarity. Reports from the Psychological Laboratories 103, University of Stockholm, 1961.

[372]

R. Ellis, D. A. Allport, G. W. Humphreys, and J. Collis. Varieties of object constancy. Q. Journal Exp. Psychol., 41A:775-796, 1989.

[373]

J. L. Elman. Finding structure in time. Cognitive Science, 14:179-211, 1990.

[374]

M. W. Fahle, S. Edelman, and T. Poggio. Fast perceptual learning in hyperacuity. Vision Research, 35:3003-3013, 1995.

[375]

M. W. Fahle and S. Edelman. Long-term learning in vernier acuity: influence of stimulus orientation, range and of feedback. Vision Research, 33:397-412, 1993.

[376]

M. W. Fahle and T. Poggio. Visual hyperacuity: spatiotemporal interpolation in human vision. Proceedings of the Royal Society of London B, 213:451-477, 1981.

[377]

M. W. Fahle. Non-fusable stimuli and the role of binocular inhibition in normal and pathological vision, especially strabismus. Documenta Ophthalmologica, 55:323-340, 1983.

[378]

M. W. Fahle. Parallel, semi-parallel, and serial processing of visual hyperacuity. In Proc. SPIE Conf. on Electronic Imaging: science and technology, Santa Clara, CA, February 1990. also in Vision Reseach 31, 2149-2184 (1991).

[379]

S. E. Fahlman. NETL: a system for representing and using real-world knowledge. MIT Press, Cambridge, MA, 1979.

[380]

J.-C. Falmagne. Elements of psychophysical theory. Clarendon Press, Oxford, 1985.

[381]

T. J. Fan, G. Medioni, and R. Nevatia. Recognizing 3D objects using surface descriptions. In Proceedings of the 2nd International Conference on Computer Vision, pages 474-481, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[382]

J. D. Farmer, N. H. Packard, and A. S. Perelson. The immune system, adaptation, and machine learning. Physica D, 22:187-204, 1986.

[383]

H. Feigl. The 'Mental' and the 'Physical'. In H. Feigl, M. Scriven, and G. Maxwell, editors, Concepts, theories, and the mind-body problem. U. of Minnesota Press, Minneapolis. MN, 1958.

[384]

J. A. Feldman and D. H. Ballard. Connectionist models and their properties. Cognitive Science, 6:205-254, 1982.

[385]

L. B. Feldman and S. Bentin. Morphological analysis of disrupted morphemes. Q. Journal Exp. Psychol, 47A:407-435, 1994.

[386]

J. Feldman. Constructing perceptual categories. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pages 244-250, 1992.

[387]

D. J. Felleman and D. C. Van Essen. Distributed hierarchial processing in primate cerebral cortex. Cerebral Cortex, 1:1-47, 1991.

[388]

M. Fendick and G. Westheimer. Effects of practice and the separation of test targets on foveal and perifoveal hyperacuity. Vision Research, 23:145-150, 1983.

[389]

M. Ferraro and T. M. Caelli. Lie transformation groups, integral transforms, and invariant pattern recognition. Spatial Vision, 8:33-44, 1994.

[390]

D. J. Field, A. Hayes, and R. F. Hess. Contour integration by the human visual system: evidence for a local association field. Vision Research, 33:173-193, 1993.

[391]

D. J. Field. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America, A 4:2379-2394, 1987.

[392]

D. J. Field. Scale-invariance and self-similar wavelet transforms: An analysis of natural scenes and mammalian visual systems. In M. Farge, J. Hunt, and T. Vassilicos, editors, Wavelets, Fractals and Fourier Transforms: New Developments and new applications, pages 151-193. Oxford University Press, 1993.

[393]

D. J. Field. What is the goal of sensory coding? Neural Computation, 6:559-601, 1994.

[394]

S. Finch and N. Chater. A hybrid approach to the automatic learning of linguistic categories. available via ftp from archive.cis.ohio-state.edu as /pub/neuroprose/finch.hybrid.ps.Z, October 1991.

[395]

D. J. Finney. Probit analysis. Cambridge University Press, Cambridge, 1971.

[396]

A. Fiorentini and N. Berardi. Perceptual learning specific for orientation and spatial frequency. Nature, 287:453-454, 1981.

[397]

M. A. Fischler and R. C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24:381-395, 1981.

[398]

M. A. Fischler and O. Firschein, editors. Readings in computer vision: issues, problems, principles and paradigms. Morgan Kaufmann, Los Altos, CA, 1987.

[399]

J. Fiser, I. Biederman, and E. E. Cooper. To what extent can matching algorithms based on direct outputs of spatial filters account for human shape recognition? Spatial Vision, 10:237-271, 1997.

[400]

W. T. Fishback. Projective and Euclidean Geometry. Wiley, New York, 1969.

[401]

C. B. Fisher and M. P. Fracasso. The Goldmeier effect in adults and children: environmental, retinal, and phenomenal influences on judgements of visual symmetry. Perception, 16:29-39, 1987.

[402]

M. J. Flannagan, L. S. Fried, and K. J. Holyoak. Distributional expectations and the induction of category structure. Journal of Experimental Psychology: Learning, Memory and Cognition, 12:241-256, 1986.

[403]

T. Flash and E. Henis. Arm trajectory modification during reaching towards visual targets. J. Cog. Neurosci., 3:220-230, 1991.

[404]

T. Flash and N. Hogan. The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci., 5:1688-1703, 1985.

[405]

T. E. Flick and L. E. Jones. A combinatorial approach for classification of patterns with missing information and random orientation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:482-490, 1986.

[406]

J. A. Fodor. RePresentations. MIT Press, Cambridge, MA, 1981.

[407]

J. A. Fodor. The modularity of mind. MIT Press, Cambridge, MA, 1983.

[408]

J. A. Fodor. Psychosemantics. MIT Press, Cambridge, MA, 1987.

[409]

P. Foldiak. Learning invariance from transformation sequences. Neural Computation, 3:194-200, 1991.

[410]

K. I. Forster. Lexical acquisition and the modular lexicon. Language and cognitive processes, 1:87-108, 1985.

[411]

K. I. Forster. Masked priming with graphemically related forms: repetition or partial activation? Quarterly J. Exp. Psychol., 39A:211-251, 1987.

[412]

D. Forsyth, J. L. Mundy, A. Zisserman, and C. M. Brown. Invariance - a new framework for vision. In Proceedings of the 3rd International Conference on Computer Vision, pages 598-605, Osaka, 1990. IEEE, Washington, DC.

[413]

D. Forsyth and A. Zisserman. Mutual illumination. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition, pages 466-473, San-Diego, CA, 1989.

[414]

D. H. Foster and P. A. Ward. Asymmetries in oriented-line detection indicate two orthogonal filters in early vision. Proceedings of the Royal Society of London B, 243:75-81, 1991.

[415]

D. H. Foster. A hypothesis connecting visual pattern recognition and apparent motion. Kybernetik, 13:151-154, 1973.

[416]

W. T. Freeman and E. H. Adelson. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:891-906, 1991.

[417]

W. T. Freeman. Exploiting the generic view assumption to estimate scene parameters. In Proceedings of the 3rd International Conference on Computer Vision, pages 347-356, Washington, DC, 1993. IEEE.

[418]

J. J. Freyd. Five hunches about perceptual processes and dynamic representations. In D. E. Meyer and S. Kornblum, editors, Attention and Performance, volume XIV, chapter 5, pages 99-119. MIT Press, 1993.

[419]

L. S. Fried and K. J. Holyoak. Induction of category distributions: a framework for classification learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 10:234-257, 1984.

[420]

J. Friedman. Flexible metric nearest neighbor classification. Technical report, Stanford University, 1994.

[421]

J. P. Frisby. Seeing. Oxford University Press, Oxford, 1979.

[422]

K. J. Friston. Imaging cognitive anatomy. Trends in Cognitive Sciences, 1:21-27, 1997.

[423]

R. Frost, K. I. Forster, and A. Deutsch. What can we learn from the morphology of Hebrew. -, -:--, 1995. submitted.

[424]

I. Fujita, K. Tanaka, M. Ito, and K. Cheng. Columns for visual features of objects in monkey inferotemporal cortex. Nature, 360:343-346, 1992.

[425]

K. Fukushima. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks, 1:119-130, 1988.

[426]

W. Gale, K. Church, and D. Yarowsky. A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26:415-439, 1992.

[427]

E. Galin and S. Akkouche. Métamorphose d'objets tridimensionnels: quelques méthodes d'accélération. Revue Techniques et Sciences Informatiques, 15:329-350, 1996.

[428]

C. R. Gallistel. The organization of learning. MIT Press, Cambridge, MA, 1990.

[429]

E. Gamble, D. Geiger, T. Poggio, and D. Weinshall. Labeling edges and the integration of low-level visual modules. IEEE Trans. SMC, 19(6), 1989.

[430]

C. P. Garbin. Visual-touch perceptual equivalence for shape information in children and adults. Perception and Psychophysics, 48:271-279, 1990.

[431]

M. Gasser. Transfer in a connectionist model of the acquisition of morphology. CogSci TR 147, Indiana University, Bloomington, IN, 1995. an expanded version of a paper presented at the Morphology Workshop, Nijmegen, June 13, 1995.

[432]

I. Gauthier and M. J. Tarr. Becoming a `Greeble' expert: Exploring the face recognition mechanism. Vision Research, 37:1673-1682, 1997.

[433]

S. Geman, D. Geman, and C. Graffigne. Locating texture and object boundaries. In P. A. Devijver and J. Mittler, editors, Pattern recognition theory and applications. Springer-Verlag, Heidelberg, 1987.

[434]

S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721-741, 1984.

[435]

S. Geman and C.-R. Hwang. Nonparametric maximum likelihood estimation by the method of sieves. Annals of statistics, 10:400-414, 1982.

[436]

S. Geman. Minimum Description Length priors for object recognition. In Challenging the frontiers of knowledge using statistical science (Proc. JSM'96), 1996. in press.

[437]

A. Georgopoulos, J. T. Lurito, M. Petrides, A. B. Schwartz, and J. T. Massey. Mental rotation of the neuronal population vector. Science, 243:234-236, 1988.

[438]

P. C. Gerhardstein and I. Biederman. 3D orientation invariance in visual object recognition. Invest. Ophthalm. Vis. Science Suppl., 32:338, 1991.

[439]

A. Gersho and R. M. Gray. Vector quantization and signal compression. Kluwer Academic Publishers, Boston, 1992.

[440]

G. M. Ghose, R. D. Freeman, and I. Ohzawa. Local intracortical connections in the cat's visual cortex: postnatal development and plasticity. J. Neurophysiol., 72:1290-1303, 1994.

[441]

J. J. Gibson. Adaptation, after-effect, and contrast in the perception of curved lines. J. Exp. Psychol., 16:1-31, 1933.

[442]

J. J. Gibson. The perception of the visual world. Houghton Mifflin, Boston, MA, 1950.

[443]

J. J. Gibson. The senses considered as perceptual systems. Houghton Mifflin, Boston, MA, 1966.

[444]

J. J. Gibson. The ecological approach to visual perception. Houghton Mifflin, Boston, MA, 1979.

[445]

Z. Gigus, J. Canny, and R. Seidel. Efficiently computing and representing aspect graphs of polyhedral objects. In Proceedings of the 2nd International Conference on Computer Vision, pages 30-39, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[446]

C. D. Gilbert. Neuronal and synaptic organization in the cortex. In P. Rakic and W. Singer, editors, Neurobiology of Neocortex, pages 219-240. Wiley, New York, NY, 1988.

[447]

C. D. Gilbert. Neuronal dynamics and perceptual learning. Current Biology, 4:627-629, 1994.

[448]

A. L. Gilchrist. Perceived lightness depends on perceived spatial arrangement. Science, 195:185-187, 1977.

[449]

F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7:219-269, 1995. (PostScript)

[450]

F. Girosi and T. Poggio. Networks and the best approximation property. A.I. Memo 1164, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1990.

[451]

M. A. Gluck and R. Granger. Computational models of the neural bases of learning and memory. Ann. Rev. Neurosci., 16:667-706, 1993.

[452]

C. Goad. Fast 3D model-based vision. In A. P. Pentland, editor, From pixels to predicates, pages 371-391. Ablex, Norwood, NJ, 1986.

[453]

K. Goebel and W. A. Kirk. Topics in metric fixed point theory. Number 28 in Cambridge studies in advanced mathematics. Cambridge Univ. Press, 1990.

[454]

R. L. Goldstone. The role of similarity in categorization: providing a groundwork. Cognition, 52:125-157, 1994.

[455]

M. A. Goodale, A. D. Milner, L. S. Jakobson, and D. P. Carey. A neurological dissociation between perceiving objects and grasping them. Nature, 349:154-156, 1991.

[456]

N. Goodman. The structure of appearance. Reidel, Dordrecht, 1977.

[457]

W. J. Gordon and J. A. Wixom. Shepard's method of "Metric Interpolation" to bivariate and multivariate interpolation. Mathematics of Computation, 32:253-264, 1978.

[458]

R. Granger and G. Lynch. Higher olfactory processes: perceptual learning and memory. Current Opinion in Neurobiology, 1:209-214, 1991.

[459]

P. Grassberger and I. Procaccia. Measuring the strangeness of strange attractors. Physica, 9D:189-208, 1983.

[460]

C. M. Gray, P. König, A. K. Engel, and W. Singer. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338:334-337, 1989.

[461]

D. M. Green and J. A. Swets. Signal detection theory and psychophysics. Wiley, New York, 1966.

[462]

R. L. Gregory. Illusions and hallucinations. In E. C. Carterette and M. P. Friedman, editors, Handbook of Perception, volume IX, pages 337-357. Academic Press, New York, NY, 1978.

[463]

R. A. M. Gregson and L. A. Britton. The size-weight illusion in 2D nonlinear psychophysics. Perception and Psychophysics, 48:343-356, 1990.

[464]

R. A. M. Gregson. Psychometrics of similarity. Academic Press, New York, 1975.

[465]

R. A. M. Gregson. Nonlinear psychophysical dynamics. Erlbaum, Hillsdale, NJ, 1988.

[466]

K. Grill Spector, S. Edelman, and R. Malach. Anatomical origin and computational role of diversity in the response properties of cortical neurons. In D. S. Touretzky G. Tesauro and T. K. Leen, editors, Advances in Neural Information Processing 7, pages 117-124. MIT Press, 1995.

[467]

K. Grill-Spector, T. Hendler, T. Kushnir, I. Kahn, S. Edelman, Y. Itzchak, and R. Malach. Hierarchy of visual object-processing stages revealed in human occipital lobe: an fMRI study, 1996.

[468]

K. Grill-Spector, T. Kushnir, T. Hendler, S. Edelman, Y. Itzchak, and R. Malach. A sequence of early object processing stages revealed by fMRI in human occipital lobe. 1997. submitted.

[469]

J. Grimes. On the failure to detect changes in scenes across saccades. In Kathleen Akins, editor, Perception, volume 5 of Vancouver Studies in Cognitive Science, chapter 4. Oxford University Press, New York, 1995.

[470]

W. E. L. Grimson and T. Lozano-Pérez. Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9:469-482, 1987.

[471]

W. E. L. Grimson. From Images to Surfaces. MIT Press, Cambridge, MA, 1981.

[472]

W. E. L. Grimson. Computational experiments with a feature-based stereo algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7:17-34, 1985.

[473]

W. E. L. Grimson. Model-Based Vision. MIT Press, Cambridge, MA, 1990.

[474]

C. G. Gross, C. E. Rocha-Miranda, and D. B. Bender. Visual properties of cells in inferotemporal cortex of the macaque. J. Neurophysiol., 35:96-111, 1972.

[475]

C. G. Gross and M. Mishkin. The neural basis of stimulus equivalence across retinal translation. In S. Harnad, R. W. Doty, L. Goldstein, J. Jaynes, and G. Krauthamer, editors, Lateralization in the nervous system. Academic Press, New York, NY, 1977.

[476]

L. Guthrie, J. Pustejovsky, Y. Wilks, and B. Slator. The role of lexicons in natural language processing. Communications of the ACM, 39:63-72, 1996.

[477]

J. Gyoba, T. Yanagida, and S. Akamatsu. View-dependent and view-independent properties in human object recognition. Electronics and Communications in Japan (Part 3), 79:158-165, 1996.

[478]

J. Hadamard. Lectures on the Cauchy problem in linear partial differential equations. Yale University Press, New Haven, CT, 1923.

[479]

P. J. B. Hancock, R. J. Baddeley, and L. S. Smith. The principal components of natural images. Network, 3:61-70, 1992.

[480]

P. J. B. Hancock, A. M. Burton, and V. Bruce. Face processing: human perception and principal components analysis. Memory and Cognition, 24:26-40, 1996. in press. (PostScript)

[481]

D. J. Hand. Discrimination and classification. Wiley, New York, 1981.

[482]

S. J. Hanson and M. A. Gluck. Spherical units as dynamic consequential regions: implications for attention, competition and categorization. In Stephen José Hanson, Jack D. Cowan, and C. Lee Giles, editors, Advances in Neural Information Processing Systems 5, pages 656-664. Morgan Kaufmann, 1993.

[483]

R. M. Haralick and J. S. Lee. The facet approach to optic flow. In L. Baumann, editor, Proceedings Image Understanding Workshop, pages 84-93, McLean, VA, 1983. Scientific Applications International Corporation.

[484]

R. M. Haralick. Statistical and structural approaches to texture. Proceedings of the IEEE, 67:786-804, 1979.

[485]

R. M. Haralick. Digital step edges from zero crossings of second directional derivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:58-68, 1984.

[486]

S. Harnad, editor. Categorical Perception: The Groundwork of Cognition. Cambridge University Press, New York, 1987.

[487]

S. Harnad. The symbol grounding problem. Physica D, 42:335-346, 1990.

[488]

C. S. Harris and A. R. Gibson. Is orientation-specific color adaptation in human vision due to edge detectors, afterimages, or "dipoles"? Science, 162:1506-1507, 1968.

[489]

C. S. Harris. Insight or out of sight?: two examples of perceptual plasticity in the human adult. In C. S. Harris, editor, Visual Coding and Adaptability, pages 95-149. Erlbaum, Hillsdale, NJ, 1980.

[490]

H. K. Hartline. The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am. J. Physiol., 121:400-415, 1938.

[491]

E. J. Hartman, J. D. Keeler, and J. M. Kowalski. Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2:210-215, 1990.

[492]

M. E. Hasselmo, M. A. Wilson, B. P. Anderson, and J. M. Bower. Associative memory function in piriform (olfactory) cortex: computational modeling and neuropharmacology. Cold Spring Harbor Symposia on Quantitative Biology, LV:599-610, 1990.

[493]

B. F. Hatfield. Introduction to quantum field theory, path integrals, and strings. In S. T. Yau, editor, Mathematical aspects of string theory, volume 1 of Advanced series in mathematical physics, chapter 1, pages 1-12. World Scientific, Singapore, 1987.

[494]

D. Haussler. Generalizing the PAC model for neural net and other learning applications. UCSC-CRL 89-30, U. of California, Santa Cruz, 1989.

[495]

D. Haussler. Decision theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation, 100:78-150, 1992.

[496]

M J Hawken and A J Parker. Spatial properties of neurons in the monkey striate cortex. Proc. R. Soc. Lon. B, 231:251-288, 1987.

[497]

R. D. Hawkins, T. W. Abrams, T. J. Carew, and E. R. Kandel. A cellular mechanism of classical conditioning in aplysia: Activity-dependent amplification of presynaptic facilitation. Science, 219:400-404, 1983.

[498]

Jr. Hayes, K. C. Reading handwritten words using hierarchical relaxation. Computer Vision, Graphics, and Image Processing, 14:344-364, 1980.

[499]

S. M. Haynes and R. Jain. A qualitative approach for recovering depth in dynamic scenes. In Proc. IEEE Workshop on Computer Vision, pages 66-71, Washington, DC, 1987. IEEE.

[500]

Z. J. He and K. Nakayama. Surfaces versus features in visual search. Nature, 359:231-233, 1992.

[501]

D. O. Hebb. The organization of behavior. Wiley, 1949.

[502]

D. Heeger and G. Hager. Egomotion and the stabilized world. In Proceedings of the 2nd International Conference on Computer Vision, pages 435-440, Tarpon Springs, FL, 1988. IEEE, Washington, DC.

[503]

D. Heeger. Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9:181-198, 1992.

[504]

Y. Hel-Or and S. Edelman. A new approach to qualitative stereo. In S. Ullman and S. Peleg, editors, Proc. 12th ICPR, pages 316-320, Jerusalem, Oct. 1994 1994. IEEE Press.

[505]

Y. Hel-Or and P. C. Teo. A common framework for steerability, motion estimation, and invariant feature detection, January 1996. submitted.

[506]

R. Held, H. W. Leibowitz, and H.-L. Teuber, editors. Handbook of sensory physiology: Perception. Springer-Verlag, Berlin, 1978.

[507]

R. Held and A. V. Hein. Adaptation of disarranged hand-eye coordination contingent upon re-afferent stimulation. Perceptual and Motor Skills, 8:87-90, 1958.

[508]

J. Henderson and M. Marcus. Description based parsing in a connectionist network. Technical Report IRCS Report 94-12, U. of Pennsylvania, September 1994.

[509]

L. Henderson, editor. Orthographies and Reading : Perspectives from Cognitive Psychology, Neuropsychology and Linguistics. Erlbaum, Hillsdale, NJ, 1984.

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