Region-Based Segmentation versus Edge Detection

TLDR: This paper compares the main approaches of partitioning an image into regions by using gray values and concludes that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference.

Abstract: This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally as the boundary between adjacent parts of an image. A formal definition is elusive, but edge detection is nonetheless a useful and ubiquitous image processing task. After comparing we have come to a conclusion that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference. The region-segmentation in spite of improving multi-spectral images has the drawback of being applied only on closed boundaries. To reach the result of edge detection we have used the technique of performance metrics and Canny edge detection. We have applied Canny ground truth to acquire more features via displaying more details.