Artificial Neural Networks for Computer Vision

Describing artificial neural network-based algorithms for early vision, this text focuses on several important early vision problems such as static and motion stereo, motion estimation and restoration, and emphasizes finding effective solutions to these problems using ANN. Many practical real-time image data are provided, and although the book is aimed at researchers and engineers, it provides an introduction to both neural networks and computer vision. A Masters degree in electrical engineering, computer science, physics or mathematics should be sufficient to follow most of the topics discussed.

[1]  Shun-ichi Amari,et al.  Characteristics of randomly connected threshold-element networks and network systems , 1971 .

[2]  Jidi Majia,et al.  Contrast , 1908, The Fairchild Books Dictionary of Fashion.

[3]  Ellen C. Hildreth,et al.  Measurement of Visual Motion , 1984 .

[4]  B. Julesz Binocular depth perception of computer-generated patterns , 1960 .

[5]  T. Wiesel,et al.  Functional architecture of macaque monkey visual cortex , 1977 .

[6]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[7]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[8]  Minoru Asada,et al.  A Motion Stereo Method Based on Coarse-to-Fine Control Strategy , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[10]  D. Marr,et al.  Smallest channel in early human vision. , 1980, Journal of the Optical Society of America.

[11]  Roland T. Chin,et al.  Shape from texture using the Wigner distribution , 1990, Comput. Vis. Graph. Image Process..

[12]  R. Chellappa,et al.  Neural Network Approach To Stereo Matching , 1988, Optics & Photonics.

[13]  J. Bergen,et al.  A four mechanism model for threshold spatial vision , 1979, Vision Research.

[14]  G. Lorentz Approximation of Functions , 1966 .

[15]  Alexander A. Sawchuk,et al.  Dynamic Optical Interconnections For Parallel Processors , 1986, Photonics West - Lasers and Applications in Science and Engineering.

[16]  J. D. Robbins,et al.  Motion-compensated television coding: Part I , 1979, The Bell System Technical Journal.

[17]  Dennis Gabor,et al.  Theory of communication , 1946 .

[18]  Y. T. Zhou,et al.  Neural network algorithms for motion stereo , 1989, International 1989 Joint Conference on Neural Networks.

[19]  J. Woods,et al.  Kalman filtering in two dimensions: Further results , 1981 .

[20]  J. P. Lasalle The stability and control of discrete processes , 1986 .

[21]  B. Julesz Foundations of Cyclopean Perception , 1971 .

[22]  B. K. Jenkins,et al.  Image restoration using a neural network , 1988, IEEE Trans. Acoust. Speech Signal Process..

[23]  R. Chellappa,et al.  Digital image restoration using spatial interaction models , 1982 .

[24]  B. R. Hunt,et al.  Digital Image Restoration , 1977 .

[25]  Rama Chellappa,et al.  Stereo matching using a neural network , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[26]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[27]  S. Ullman The Interpretation of Visual Motion , 1979 .

[28]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[29]  Christoph von der Malsburg,et al.  Pattern recognition by labeled graph matching , 1988, Neural Networks.

[30]  Jin Luo,et al.  Computing motion using analog and binary resistive networks , 1988, Computer.

[31]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[32]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[33]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[34]  Bir Bhanu,et al.  Segmentation of natural scenes , 1987, Pattern Recognit..

[35]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Narendra Ahuja,et al.  EXTRACTING SURFACES FROM STEREO IMAGES: AN INTEGRATED APPROACH. , 1987 .

[37]  Shun-ichi Amari,et al.  Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements , 1972, IEEE Transactions on Computers.

[38]  M. M. Sondhi,et al.  Image restoration: The removal of spatially invariant degradations , 1972 .

[39]  J. Tigges,et al.  Areal and laminar distribution of neurons interconnecting the central visual cortical areas 17, 18, 19, and MT in squirrel monkey (Saimiri) , 1981, The Journal of comparative neurology.

[40]  John E. W. Mayhew,et al.  Psychophysical and Computational Studies Towards a Theory of Human Stereopsis , 1981, Artif. Intell..

[41]  Gene Gindi,et al.  OPTICAL NEUROCOMPUTER FOR IMPLEMENTATION OF THE MARR-POGGIO STEREO ALGORITHM. , 1987 .

[42]  R E Weller,et al.  Cortical connections of striate cortex in the owl monkey , 1982, The Journal of comparative neurology.

[43]  Parvati Dev,et al.  Perception of Depth Surfaces in Random-Dot Stereograms: A Neural Model , 1975, Int. J. Man Mach. Stud..

[44]  V. Montero Patterns of connections from the striate cortex to cortical visual areas in superior temporal sulcus of macaque and middle temporal gyrus of owl monkey , 1980, The Journal of comparative neurology.

[45]  Valdis Berzins,et al.  Dynamic Occlusion Analysis in Optical Flow Fields , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  W. Eric L. Grimson,et al.  Computational Experiments with a Feature Based Stereo Algorithm , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  N M Grzywacx,et al.  Motion correspondence and analog networks , 1987 .

[48]  Rama Chellappa,et al.  A network for motion perception , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[49]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[50]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Stephen T. Barnard,et al.  A Stochastic Approach to Stereo Vision , 1986, AAAI.

[52]  Demetri Psaltis,et al.  Optical Neural Computers , 1987, Topical Meeting on Optical Computing.

[53]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[54]  Eric L. W. Grimson,et al.  From Images to Surfaces: A Computational Study of the Human Early Visual System , 1981 .

[55]  J. Goodman,et al.  Neural networks for computation: number representations and programming complexity. , 1986, Applied optics.

[56]  Steven D. Blostein,et al.  QUANTIZATION ERRORS IN STEREO TRIANGULATION. , 1987 .

[57]  Jan Biemond,et al.  Boundary value problem in image restoration , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[58]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[59]  Teuvo Kohonen,et al.  Associative memory. A system-theoretical approach , 1977 .

[60]  T. Poggio Vision by man and machine. , 1984, Scientific American.

[61]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[62]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[63]  J. D. Robbins,et al.  Motion-compensated coding: Some new results , 1980, The Bell System Technical Journal.

[64]  T. D. Williams,et al.  Depth from camera motion in a real world scene , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[65]  Joseph W. Goodman,et al.  The Optical Data Processing Family Tree , 1984 .

[66]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[67]  R. Desimone,et al.  Columnar organization of directionally selective cells in visual area MT of the macaque. , 1984, Journal of neurophysiology.

[68]  S. Negahdaripour,et al.  Relaxing the Brightness Constancy Assumption in Computing Optical Flow , 1987 .

[69]  Ramesh C. Jain,et al.  Motion Stereo Using Ego-Motion Complex Logarithmic Mapping , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[70]  Robert C. Bolles,et al.  Epipolar-plane image analysis: a technique for analyzing motion sequences , 1987 .

[71]  Petr Beckmann,et al.  Orthogonal polynomials for engineers and physicists , 1973 .

[72]  Tomaso Poggio,et al.  Cooperative computation of stereo disparity , 1988 .

[73]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[74]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[75]  D Psaltis,et al.  Optical implementation of the Hopfield model. , 1985, Applied optics.

[76]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[77]  Rodney A. Brooks,et al.  Model-Based Computer Vision , 1984 .

[78]  N M Grzywacz,et al.  Massively parallel implementations of theories for apparent motion. , 1988, Spatial vision.

[79]  B. O'neill Elementary Differential Geometry , 1966 .

[80]  Terrence J. Sejnowski,et al.  NETtalk: a parallel network that learns to read aloud , 1988 .

[81]  C. Atkinson METHODS FOR SOLVING INCORRECTLY POSED PROBLEMS , 1985 .

[82]  Bernard Widrow,et al.  Adaptive sampled-data systems , 1960 .

[83]  J. Aloimonos Shape from texture , 1988, Biological cybernetics.

[84]  J. J. Gerbrands,et al.  A fast Kalman filter for images degraded by both blur and noise , 1983 .

[85]  John J. Hopfield,et al.  CONCENTRATION INFORMATION IN TIME: ANALOG NEURAL NETWORKS WITH APPLICATIONS TO SPEECH RECOGNITION PROBLEMS. , 1987 .

[86]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[87]  Takeo Kanade,et al.  The 3D MOSAIC Scene Understanding System , 1983, IJCAI.

[88]  H. Wilson,et al.  Threshold visibility of frequency gradient patterns , 1977, Vision Research.

[89]  Claude L. Fennema,et al.  Velocity determination in scenes containing several moving objects , 1979 .

[90]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[91]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .