Distinct Multicolored Region Descriptors for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast edge integration based active contours for color images
Computers and Electrical Engineering
Geometric active contours without re-initialization for image segmentation
Pattern Recognition
A geometric active contour model without re-initialization for color images
Image and Vision Computing
Novel classification and segmentation techniques with application to remotely sensed images
Transactions on rough sets VII
Video compression schemes using edge feature on wireless video sensor networks
Journal of Electrical and Computer Engineering
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Edge detection is a useful task in low-level image processing. The efficiency of many image processing and computer vision tasks depends on the perfection of detecting meaningful edges. To get a meaningful edge, thresholding is almost inevitable in any edge detection algorithm. Many algorithms reported in the literature adopt ad hoc schemes for this purpose. These algorithms require the threshold values to be supplied and tuned by the user. There are many high-level tasks in computer vision which are to be performed without human intervention. Thus, there is a need to develop a scheme where a single set of threshold values would give acceptable results for many color images. In this paper, an attempt has been made to devise such an algorithm. Statistical variability of partial derivatives at each pixel is used to obtain standardized edge magnitude and is thresholded using two threshold values. The advantage of standardization is evident from the results obtained