Towards a method for parametrizing models of early vision using psychophysical data

Parametrizing computational models of vision, i.e. choosing values for their free parameters, is important both for applied computer vision and for developing the models themselves. A new, principled technique for parametrizing computational models of early vision is presented. It requires that some specific mathematical relationships are found between properties of the input to a model and some suitable psychophysical data. Comparing these either parametrizes the model, allowing it to be implemented as a computer vision system, or refutes it, motivating further modelling. The application of the technique to two computational models of early vision is demonstrated.

[1]  Jack F. Gerrissen,et al.  On the network-based emulation of human visual search , 1991, Neural Networks.

[2]  Heiko Neumann,et al.  A Contrast- and Luminance-driven Multiscale Network Model of Brightness Perception , 1995, Vision Research.

[3]  Keith Price,et al.  Anything you can do, I can do better (No you can't) , 1986, Comput. Vis. Graph. Image Process..

[4]  R. Watt,et al.  A theory of the primitive spatial code in human vision , 1985, Vision Research.

[5]  Alan N. Gove,et al.  Brightness perception, illusory contours, and corticogeniculate feedback , 1995, Visual Neuroscience.

[6]  Anne Treisman,et al.  Features and objects in visual processing , 1986 .

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

[8]  Robert M. Haralick,et al.  Performance Characterization in Computer Vision , 1993, BMVC.

[9]  L. Segel.  SIMPLIFICATION AND SCALING , 1972 .

[10]  Iain D. Gilchrist,et al.  Psychophysical analyses of contour processing in humans: the case for qualitative tests , 1998, Image Vis. Comput..

[11]  Peter Meer,et al.  Computer vision: the goal and the means , 1994 .

[12]  S. Grossberg.  How does a brain build a cognitive code , 1980 .

[13]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  D. Hubel.  Eye, brain, and vision , 1988 .

[15]  D. Watt.  Visual Processing: Computational Psychophysical and Cognitive Research , 1990 .

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

[17]  Jonathan A. Marshall,et al.  Adaptive perceptual pattern recognition by self-organizing neural networks: Context, uncertainty, multiplicity, and scale , 1995, Neural Networks.

[18]  Rüdiger von der Heydt,et al.  Simulation of neural contour mechanisms: representing anomalous contours , 1998, Image Vis. Comput..

[19]  Robert M. Haralick.  Comments on performance characterization replies , 1994 .

[20]  R. L. de Valois,et al.  Psychophysical studies of monkey vision. 3. Spatial luminance contrast sensitivity tests of macaque and human observers. , 1974, Vision research.

[21]  Ennio Mingolla,et al.  Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations , 1985 .

[22]  Heiko Neumann,et al.  Mechanisms of Neural Architecture for Visual Contrast and Brightness Perception , 1996, Neural Networks.

[23]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[24]  Thomas S. Huang,et al.  Performance of computer vision algorithms , 1994 .

[25]  Stephen Grossberg,et al.  A neural network architecture for figure-ground separation of connected scenic figures , 1991, Neural Networks.

[26]  Virginio Cantoni,et al.  Human and Machine Perception 2 , 2012, Springer US.

[27]  Jitendra Malik,et al.  Finding Boundaries in Images , 1990, 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, 1990..

[28]  Peter C. M. Molenaar,et al.  Numerical bifurcation analysis of distance-dependent on-center off-surround shunting neural networks , 1996, Biological Cybernetics.

[29]  Josef Skrzypek,et al.  Neural network models for illusory contour perception , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Robert M. Haralick.  Methodology for experimental computer vision , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[31]  S. Grossberg,et al.  Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading. , 1985 .

[32]  S Grossberg,et al.  Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance , 1984, Perception & Psychophysics.

[33]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[34]  Heiko Neumann,et al.  An Outline of a Neural Architecture for Unified Visual Contrast and Brightness Perception , 1994 .

[35]  S. Grossberg.  How does the brain build a cognitive code , 1988 .