Contour And Boundary Detection Via Perceptual Mechanisms Of Primary Visual Cortex

Contours and boundaries that define object shape and indicate outer limits for regions. They are critical for human or computer recognition of objects. However, it is extremely difficult to extract contours from cluttered scenes automatically. To do that, three major problems need to be solved: (1) eliminating non-meaningful edges engendering from texture fields rather than object boundaries; (2) grouping local elements into meaningful global features according to context information; (3) many important structures are only implicitly determined, such as by texture boundaries, or are entirely physically absent, such as where a background is by chance the same color as a foreground object. To address these problems, we construct different models according to perceptual mechanisms of primary visual cortex and verify the performances of the models by synthetic and natural images.To order to reduce cluttered and textured elements, we present a method for suppressing texture edges via dynamic properties of recurrent inhibition in non-classical receptive field. The method deals with texture and boundary in different ways, and thus dramatically reduces non-meaningful distractor elements, while selectively retains region boundaries and isolated structures.How to organize coherent spatial configurations into salient contours is another important issue of our study. We combine a co-circularity rule with visual preference for low curvature to define a local grouping function of contour integration, which relates the axial specificity with the modular specificity. Local elements are grouped into a meaning global feature according to contextual information, thus allowing them emerge from their backgrounds.We unify the dual processes of spatial facilitation and surround inhibition in an integrated model by spatially segregated regions of excitatory and inhibitory inputs, thus allowing the model to implement multiple perceptual tasks that require opposing interactions. Here, we put some emphases on the two different roles– spatial facilitation and surround inhibition - played in the contour extraction. Inhibitory interactions are supposed to play a more important role in the segmentation of surfaces and textures, while excitatory contextual interactions are deemed to be more significant in contour integration and figure-ground segregation.Color of an image can carry much more information than gray level, which can help the image analysis process and yield better results than approaches using only gray scale information. Thus we extend the gray-level version to color image processing. The color version involves homogenous inhibition of more properties, which can more effectively remove textured edges; on the other hand, color information provides more cues for contour grouping, which can help to organize the same property.Finally, we apply the scheme to two aspects of vessel enhancement in DSA images and road detection in SAR images.