title: Standardization of edge magnitude in color images.
abstract: 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.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108, India. sarif_r@isical.ac.in
authors.forename: Sarif Kumar
authors.initials: SK
authors.lastname: Naik
authors.forename: C A
authors.initials: CA
authors.lastname: Murthy
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003116:Color; D003124:Colorimetry / Q000379:methods* / Q000592:standards; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods* / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D016247:Information Storage and Retrieval / Q000379:methods* / Q000592:standards; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes