Improving edge detection using ıntersection consistency

Çiftçi, Serdar
Edge detection is an important step in computer vision since edges are utilized by the successor visual processing stages including many tasks such as motion estimation, stereopsis, shape representation and matching, etc. In this study, we test whether a local consistency measure based on image orientation (which we call Intersection Consistency - IC), which was previously shown to improve detection of junctions, can be used for improving the quality of edge detection of seven different detectors; namely, Canny, Roberts, Prewitt, Sobel, Laplacian of Gaussian (LoG), Intrinsic Dimensionality, Line Segment Detector (LSD). IC works well on images that contain prominent objects which are different in color from their surroundings. IC give good results on natural images that have especially cluttered background. On images involving human made objects, IC leads to good results as well. But, depending on the amount of clutter, the loss of true positives might be more crucial. Through our comprehensive investigation, we show that approximately 21% increase in f-score is obtained whereas some important edges are lost. We conclude from our experiments that IC is suitable for improving the quality of edge detection in some detectors such as Canny, LoG and LSD.


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Foreground Background segmentation is a process which separates the stationary objects from the moving objects on the scene. It plays significant role in computer vision applications. In this study, several background foreground segmentation algorithms are analyzed by changing their critical parameters individually to see the sensitivity of the algorithms to some difficulties in background segmentation applications. These difficulties are illumination level, view angles of camera, noise level, and range of ...
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Citation Formats
S. Çiftçi, “Improving edge detection using ıntersection consistency,” M.S. - Master of Science, Middle East Technical University, 2011.