An Approach for the Reduction of Unwanted Edges in Contour Detection Based on Local Filtering

Abstract

In this paper, an approach for the reduction of unwanted edges in contour detection based on local filtering is presented. Our approach can be used as a preprocessing step before contour detection. Also our approach is useful for object recognition based on feature extraction tasks, because many contour detection methods can’t delete all unwanted edges carefully. Our method consists of a computational algorithm that has 7 steps. Including smoothing, edge detection, smoothing, decreasing of pixels, thresholding, local filtering, and mask creation respectively. We use smoothing for adhering neighbor edge pixels and weakening alone edge pixels. So we can amplify the correct edge pixels and attenuate unwanted edge pixels by smoothing the edge image. In local filtering, we use a proposed casual template that determines noisy regions and correct regions and therefore can create a mask matrix that its elements related to mentioned regions. Finally we can use the "mask matrix" for improving contours by using a “And” operator and we ensure final contour that has a few context effect.