An evaluation criterion for edge detection techniques in noisy images

Segmentation in noisy images is an important and difficult problem in pattern recognition. Edge detection is a crucial step in this process. Current subjective and objective methods for evaluation and comparison of segmentation techniques are inadequate or not applicable to edge detection techniques. A general framework for segmentation evaluation in noisy images is introduced after a brief review of previous work. Several measures based on similarity between true and result segmented images are defined. These measures are, then, combined in a unique criterion as a proposed global measure of performance. The results indicate that this global measure can be helpful in the evaluation and comparison of segmentation techniques applied to noisy images.