A neural model of early vision: contrast, contours, corners and surfaces: contributions toward an integrative architecture of form and brightness perception

The thesis is concerned with the functional modeling of information processing in early and midlevel vision. The mechanisms can be subdivided into two systems, a system for the processing of discontinuities (such as contrast, contours and corners), and a complementary system for the representation of homogeneous surface properties such as brightness. For the robust processing of oriented contrast signals, a mechanism of dominating opponent inhibition (DOI) is proposed and integrated into an existing nonlinear simple cell model. We demonstrate that the model with DOI can account for physiological data on luminance gradient reversal. For the processing of both natural and artificial images we show that the new mechanism results in a significant suppression of responses to noisy regions, largely independent of the noise level. This adaptive processing is further examined by a stochastic analysis and numerical evaluations. We also show that contrast-invariant orientation tuning can be achieved in a purely feedforward model based on inhibition. DOI results in a sharpening of the tuning width of model simple cells which are in accordance with empirical findings. The results lead to the proposal of a new functional role of the dominant inhibition as observed empirically, namely to sharpen the orientation tuning and to allow for robust contrast processing under suboptimal, noisy viewing conditions. For the processing of contours, a model of recurrent colinear long-range interaction in V1 is proposed. The key properties of the model are excitatory long-range interactions between cells with colinear receptive fields, inhibitory unoriented short-range interactions and modulating feedback. In the model, initial noisy feedforward responses which are part of a more global contour are enhanced, while other responses are suppressed. We show for a number of artificial and natural images that the recurrent long-range processing results in a selective enhancement of coherent activity at contour locations. The competencies of the model are further quantitatively evaluated using two measures of contour saliency and orientation significance. The model also qualitatively reproduces empirical data on surround suppression and facilitation. We further suggest and examine a model variant using early feedback of grouped responses, showing an even stronger enhancement of contour saliency compared to the standard model. These results may suggest a functional role for the layout of different feedback projections in V1. Regions of intrinsically 2D structures such as corners and junctions are important for both biological and artificial vision systems. We propose a novel scheme for the robust and reliable representation and detection of junction points, where junctions are characterized by high responses at multiple orientations within an model hypercolumn. The recurrent long-range interaction results in a robust extraction of orientation information. A measurement of circular variance is used to detect and localize junctions. We show for a number of artificial and natural images that localization accuracy and positive correctness of the junction detection is improved compared to a purely feedforward computation of contour orientations. We also use ROC analysis to compare the new scheme with two other junction detector schemes based on Gaussian curvature and the structure tensor, showing that the new approach performs superior to the standard schemes. Brightness surfaces are reconstructed by a diffusive spreading or filling-in of sparse contrast measurements, which is locally controlled by contour signals. A mechanism of confidence-based fillingin is proposed, where a confidence measure ensures a robust selection of sparse contrast signals. We show that the model with confidence-based filling-in can generate brightness surfaces which are invariant against size and shape transformations and can also generate smooth brightness surfaces even from noisy contrast data, in contrast to standard filling-in. The model can also account for psychophysical data on human brightness perception. We further suggest a new approach for the reconstruction of reference levels, where sparse contrast signals are modulated to carry an additional luminance component. We show for a number of test stimuli that the newly proposed scheme can successfully predict human brightness perception. Overall, we show that basic tasks in early vision can be robustly and efficiently implemented by biologically motivated mechanisms. This leads to a deeper understanding of the functional role of the particular mechanisms and provides the basis for practical applications in technical vision systems.