| We here develop optimal criteria for detection and localization of step edges in single look complex (SLC) synthetic aperture radar (SAR) images. By working on complex data rather than intensity images, we can easily take the speckle autocorrelation into account, obtain more accurate estimates of local mean reeectivities, and thus achieve better edge detection and edge localization than with ...

Edge detection could be an elementary and important tool in image/video processing notably within the areas of segmentation, feature extraction and tracking. The bottleneck of limited processor speed affects the image processing algorithms in software implementation. This has been overcome with the advancements in VLSI technology. The proposed work presents implementation of edge detection algo...

Edge detection is of fundamental importance in image analysis. Edges character ize object boundar ies , and are thereby very useful for registration, segmentation, and identification of objects in images. For example, an edge detector is commonly found in such applications as contour mapping and target recognition. Similar to some digital signal processing algorithms, edge detection involves tw...

First-order linear filters constitute the algorithms most widely applied to edge detection in digital images. Nevertheless they don’t allow good results to be obtained from images where the contrast varies a lot, due to non-uniform lighting, as it happens during acquisition of most part of natural images. In this paper, we evaluate the performance of a fuzzy inference system in edge detection. ...

We use an evolutionary process to seek a specialized powerful rule of Cellular Automata (CA) among a set of best rules for extracting edges in a given black-white image. This best set of local rules determines the future state of CA in an asynchronous way. The Genetic Algorithm (GA) is applied to search the best CA rules that can realize better the edge detection. Index Terms — Genetics Algorit...

Communities of vertices within a giant network such as the World Wide Web are likely to be vastly smaller than the network itself. However, Fortunato and Barthélemy have proved that modularity maximization algorithms for community detection may fail to resolve communities with fewer than √L/2 edges, where L is the number of edges in the entire network. This resolution limit leads modularity max...

-The social insect metaphor for solving diverse problems has become an emerging issue in the current years emphasizing on stochastic construction process building the solution probabilistically. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants deposits a volatile chemical call pheromone on the ground surface for the purpose of foraging and col...

This paper summarizes a system, called SED, "Selection of Edge DetectorsN, which is able to automatically select edge detectors and their scales to extract a given edge. The basic organization of the SED system is a collection of edge detectors within a knowledge structure about the characteristics of the given edge, the properties of the detectors and the mutual relation between them. The comb...

The edgelets produced by the Canny edge detector are first thresholded to reduce weak clutter. Beginning from an edgelet, we track along connected neighbours to get a list of edgelets. If one edgelet has more than two neighbours, we check all possible extensions of the curve, and segment the curves on a largest-first basis, breaking the others to form separate curves. This removes small spurs e...