Abstract-We propose a biologically motivated computational step, called nonclassical receptive field (non-CRF) inhibition, more generally surround inhibition or suppression, to improve contour detection in machine vision. Non-CRF inhibition is exhibited by 80% of the orientation-selective neurons in the primary visual cortex of monkeys and has been demonstrated to influence the visual perception of man as well. The essence of this mechanism is that the response of an edge detector in a certain point is suppressed by the responses of the operator in the region outside the area of operator support. We combine classical edge detection with two types of inhibitory mechanism, isotropic and anisotropic inhibition, both of which have counterparts in biology. For edge detection, we also use a biologically motivated method (the Gabor energy operator). The resulting operator responds strongly to isolated lines, edges, and contours, but exhibits a weaker or no response to edges that make part of texture.We use natural images with associated ground truth contour maps to assess the performance of the proposed operator regarding the detection of contours while suppressing texture edges. The results show that our method enhances contour detection in cluttered visual scenes more effectively than classical edge detectors used in machine vision (Canny edge detector). Therefore, the proposed operator is more useful for contour-based object recognition tasks, such as shape comparison, than traditional edge detectors, which do not distinguish between contour and texture edges. Traditional edge detection algorithms can, however, also be extended with surround suppression. Next to the advancement of contour detection in machine vision, this study contributes to the understanding of inhibitory mechanisms in biology.

Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

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The Max-Tree designed for morphological attribute filtering in image processing, is a data structure in which the nodes represent connected components for all threshold levels in a data set. Attribute filters compute some attribute describing the shape or size of each connected component and then decide which components to keep or to discard. In this paper, we augment the basic Max-Tree data structure such that interactive volumetric filtering and visualization becomes possible. We introduce extensions that allow (1) direct, splatting-based, volume rendering; (2) representation of the Max-Tree on graphics hardware; and (3) fast active cell selection for isosurface generation. In all three cases, we can use the Max-Tree representation for visualization directly, without needing to reconstruct the volumetric data explicitly. We show that both filtering and visualization can be performed at interactive frame rates, ranging between 2.4 and 32 frames per seconds. In contrast, a standard texture-based volume visualization method manages only between 0.5 and 1.8 frames per second. For isovalue browsing, the experimental results show that the performance is comparable to the performance of an interval tree, where our method has the advantage that both filter threshold browsing and isolevel browsing are fast. It is shown that the methods using graphics hardware can be extended to other connected filters.

Abstract. Morphological connected set filters for extraction of filamentous details from medical images are developed. The advantages of these filters are that they are shape preserving and do not amplify noise. Two approaches are compared: (i) multi-scale filtering (ii) single-step shape filtering using connected set (or attribute) thinnings. The latter method highlights all filamentous structure in a single filtering stage, regardless of the scale. The second approach is an order of magnitude faster than the first, filtering a 256 3 volume in 41.65 s on a 400 MHz Pentium II.