Biomedical subjects

G J van Tonder

Publications and source records attributed to G J van Tonder.

3 recordsLinked to original sources

PubMed2000

The patchwork engine: image segmentation from shape symmetries.

We propose blind segmentation of images into shape-related 'patches' based on pre-calculated local symmetries (Van Tonder, G.J. & Ejima, Y. (1999). (Forthcoming a) Flexible computation of shape symmetries. Submitted for publication) in shape boundary contours. First, lateral weights between all points in the boundary contour map are assigned analogous to Euclidean distance maps in watershed segmentation (Beucher, S. & Lantejoul, C. (1979). Use of watersheds in contour detection. Proceedings of the International Workshop on Image Processing, CCETT, Rennes, France.). Lateral weights are then used to: (1) extract local maxima in symmetries; (2) link maxima within locally enclosed boundary contours; and (3) reconstruct shape contours using symmetry maxima as 'seeds'. The new model overcomes weaknesses of watershed segmentation. The new model closes gaps in relatively more solid image contours, but it is fundamentally different from methods based on contour interpolation (Grossberg, S., Mingolla, E. & Todorovć, D. (1989). A neural network architecture for preattentive vision, IEEE Transactions on Biomedical Engineering 36, 65-84; Heitger, F. & von der Heydt, R. (1993). A computational model of neural contour processing: figure-ground segregation and illusory contours. Proceedings of the Fourth International Conference on Computer Vision, IEEE Computer Society Press, Washington D.C. (pp. 32-40)). Images are segmented into shape-relevant color-by-number-like patches which compare well to related methods (Gauch, J. & Pizer, M. (1993). The intensity axis of symmetry and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (8), 753-770; Ilg, W. & Ogniewicz, R. (1995). The application of Voronoi skeletons to perceptual grouping in line images, Proceedings of the 11th International Conference on Pattern Recognition, The Hague, The Netherlands, pp. 382-385; Zhu, S.C. & Yuille, A.L. (1996) FORMS: a flexible object recognition and modeling system, International Journal of Computer Vision, 20 (3), 187-212.). Two primitive operations, comparison and merging of patches, are proposed as drives for exposing more global shape contours from patches. We conclude that symmetry goes beyond abstract shape morphology: it can contribute to figure-ground segmentation in early vision and form part of primitive operations needed to create hypotheses of complex shape.

Animals↗

PubMed2000

Bottom-up clues in target finding: why a Dalmatian may be mistaken for an elephant.

We provide informal psychophysical support for a strategy where bottom-up features guide attention toward a target, and the top-down path interprets hypothetical shapes at the target location--as opposed to a dominant top-down approach. In our survey, for which we used the familiar picture of a Dalmatian dog against a dappled background, (i) 75% of subjects initially found a bulging body which overlaps that of the dog, but final 'top-down' percepts were unexpected: nearly all subjects assigned an incorrect head and limbs to the body; (ii) after random rotation of texture elements overlapping computed features only 45% of subjects reported a bulging body, with a few adding limbs etc. The picture of the Dalmatian dog must therefore contain many bottom-up features--a top-down strategy may find 'incorrect' targets at correct target locations. Computational support for these claims is more easily constructed than one may expect. We could compute at least two bottom-up features, both useful in 3-D surface interpolation from 2-D scenes, which yielded significant values at the location of the Dalmatian dog: anisotropic texture compression and affine texture distortion cues. We therefore conclude that the role of top-down processing is overstated in a traditional example such as the Dalmatian dog picture.

Cues↗

PubMed2000

From image segmentation to anti-textons.

We apply the 'patchwork engine' (PE; van Tonder and Ejima, 2000 Neural Networks forthcoming) to encode spaces between textons in an attempt to find a suitable feature representation of anti-textons [Williams and Julesz, 1991, in Neural Networks for Perception volume 1: Human and Machine Perception Ed. H Wechsler (San Diego, CA: Academic Press); 1992, Proceedings of the National Academy of Sciences of the USA 89 6531-6534]. With computed anti-textons it is possible to show that tessellation and distribution of anti-textons can differ from that of textons depending on the ratio of texton size to anti-texton size. From this we hypothesise that variability of anti-textons can enhance texture segregation, and test our hypothesis in two psychophysical experiments. Texture segregation asymmetry is the topic of the first test. We found that targets on backgrounds with regular anti-textons segregate more strongly than on backgrounds with highly variable anti-textons. This neatly complements other explanations for texture segregation asymmetry (e.g. Rubenstein and Sagi, 1990 Journal of the Optical Society of America A 7 1632-1643). Second the relative significance of textons and anti-textons in human texture segregation is investigated for a limited set of texture patterns. Subjects consistently judged a combination of texton and anti-texton gradients as more conspicuous than texton-only gradients, and judged texton-only gradients as being more conspicuous than anti-texton-only gradients. In the absence of strong texton gradients the regularity versus irregularity of anti-textons agrees with perceived texture segregation. Using PE outputs as anti-texton features thus enabled the conception of various useful tests on texture segregation. The PE is originally intended as a general image segmentation method based on symmetry axes. With this paper we therefore hope to relate anti-textons with visual processing in a wider sense.

Contrast Sensitivity↗