English

Abstract

Edges are the feature points of different objects in a digital image having dissimilar intensities. Edge detection is one of the active research area intended to highlight those pixels whose intensity changes very sharply in a digital image. This technique is employed as pre-processing stage in different application areas like Image Segmentation, Registration, feature extraction, Machine vision and many more. There are numerous approaches developed so far for edge detection but an optimal edge detector shall be designed that may perform excellently on all types of test images and shall satisfy different criteriaof selection viz., robustness, efficient use of resources and adaptability. In this paper, a novel edge detection technique implementing Back-propagation Artificial Neural Network with multi-thresholding is proposed. It is observed that the proposed neural edge detector not only computes a better qualitative analysis for non-synthetic as well as medical images but also gives promising qualitative analysis in terms of MSE, MAE & PSNR.