A New Edge Detector Using 2D Beta Distribution

Edge detection is a problem of fundamental importance in image analysis. In typical images, edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. Edge detection is traditionally implemented by convolving the image with masks. These masks are constructed using a first or second derivative operators. Thus, the problem of edge detection is therefore related to the problem of mask construction. Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limitation in its shape, it has only symmetric shape. Gaussian distribution is a private case of Beta distribution. In the paper we will use the Beta distribution to construct the masks and then detection the edge of objects in images. The constructed masks are applied to images and we obtained good results.