Paper Info
Title
A New Reference-Based Edge Map Quality Measure
Abstract
Edge detection is an important task in image processing, and the quality of further processing is often reflected by the quality of edge detector outputs. Therefore, it is necessary to develop effective edge map quality measures to assist in evaluating the performance of edge detectors. Objective evaluation measures are crucial in automatically determining the optimal edge map for a given image or an application, as well as its parameter values. In this paper, a new reference-based edge measure (RBEM) is introduced to evaluate the performance of edge detector outputs relative to a ground truth. The new measure fuses four component metrics, based on edge pixel presence, edge corner localization, thick edge occurrence, and edge connectivity. Each of these metrics can be used separately or as a standalone measure to evaluate the quality of an edge map in terms of specific characteristics. The effectiveness of the proposed measure is demonstrated for selecting the best edge detector among several edge detectors, as well as for selecting the optimal parameter values, for both synthetic images and natural images. Experimental results show that the presented RBEM outperforms the existing methods according to subjective evaluation mean opinion scores, as it considers more important visual features in its evaluation.
Year
DOI
Venue
2016
10.1109/TSMC.2015.2503386
IEEE Trans. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Image edge detection,Detectors,Thickness measurement,Measurement uncertainty,Visualization
Computer vision,Canny edge detector,Deriche edge detector,Image gradient,Pattern recognition,Computer science,Edge detection,Image processing,Ground truth,Artificial intelligence,Pixel,Detector
Journal
Volume
Issue
ISSN
PP
99
2168-2216
Citations 
PageRank 
References 
7
0.51
35
Authors
4
Name
Order
Citations
PageRank
Karen Panetta154040.40
Gao, C.2141.06
Sos Agaian36716.48
Shahan C. Nercessian4775.90