Biomedical subjects

Walter Vanzella

Publications and source records attributed to Walter Vanzella.

2 recordsLinked to original sources

PubMed2006

A versatile segmentation procedure.

In this paper, a new method for the segmentation of natural images is proposed. Original images g(x, y) are first regularized by using a self-adaptive implementation of the Mumford-Shah functional so that the two parameters alpha and gamma controlling the smoothness and fidelity, automatically adapt to the local scale and contrast of g(x, y). From the regularized image u(x, y) which is piecewise smooth, it is possible to obtain a piecewise constant image sN(x, y) representing a segmentation of the original image g(x, y). Indeed, sN(X, y) is the union of N closed regions, having a constant grey level, preserving thin bars and trihedral junctions present in the original image g(x, y). If the number N of closed regions is too high, closed regions can be merged by minimizing a functional which depends on a parameter n. When n is set equal to 1, a coarse segmentation is obtained with a few tens of distinct regions. With larger values of n, finer segmentations are obtained with about a hundred distinct regions. Therefore, by selecting the value of n it is possible to obtain segmentations at different resolutions. The proposed method for image segmentation was evaluated in two cases where a ground truth segmentation is available. The proposed procedure for image segmentation is rather versatile and depends on only one parameter n and seems suitable for higher level processing, such as categorization, recognition, and scene understanding.

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PubMed2004

Edge detection revisited.

The present manuscript aims at solving four problems of edge detection: the simultaneous detection of all step edges from a fine to a coarse scale; the detection of thin bars with a width of very few pixels; the detection of trihedral junctions; the development of an algorithm with image-independent parameters. The proposed solution of these problems combines an extensive spatial filtering with classical methods of computer vision and newly developed algorithms. Step edges are computed by extracting local maxima from the energy summed over a large bank of directional odd filters with a different scale. Thin roof edges are computed by considering maxima of the energy summed over narrow odd and even filters along the direction providing maximal response. Junctions are precisely detected and recovered using the output of directional filters. The proposed algorithm has a threshold for the minimum contrast of detected edges: for the large number of tested images this threshold was fixed equal to three times the standard deviation of the noise present in usual acquisition system (estimated to be between 1 and 1.3 gray levels out of 256), therefore, the proposed scheme is in fact parameter free. This scheme for edge detection performs better than the classical Canny edge detector in two quantitative comparisons: the recovery of the original image from the edge map and the structure from motion task. As the Canny detector in previous comparisons was shown to be the best or among the best detectors, the proposed scheme represents a significant improvement over previous approaches.

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