Edge Detection Revisited

Abstract

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.

Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image

“…Canny operator detects edges in all possible direction but the implementation is very complex and very difficult [23], [24].…”

“…Above discuss techniques give a rough idea about the shape, size and location of the suspicious area [24]. Result of the above technique is used for calculating features, which further helps in classifying the suspicious area and gives a conformation about the disease.…”

Region Quad-Tree Decomposition Based Edge Detection for Medical Images

“…If quadtree decomposition is performed over the images, the leaves of the quadtree or the level above the leaves will represent a maximum intensity of these pixels. By using quadtree, we can eliminate the pixels which do not represent the edges, and post-process only the leaves and their parents from the quadtree decomposed image which are 1x1 and 2x2 blocks using the normal differentiation technique along with other edge detection techniques such as Canny [9], Roberts [3], Sobel [10], and Prewitt [11] to obtain the edges. This approach is advantageous when working with huge images that are already quadtree decomposed.…”

“…Since edges correspond to variations in several properties, different types of edges might occur in an image. The most common types of such edges are step edges, line edges, and junctions [3,4].…”

Perspective methods of human identification: Ear biometrics

“…Contours within images may contain important information allowing object description, representation, and calculation of parameters enabling object recognition [22,23]. Similarly, contours extracted from ear images describe major information included in ear image.…”