Parallel Algorithms for Edge Detection in an Image

Edge detection is an important process in image segmentation, object recognition, template matching, etc. It computes gradients in both horizontal and vertical directions of the image at each pixel position to find the image boundaries. The conventional edge detectors take significant time to detect the edges in the image. To reduce the computational time, this paper proposes parallel algorithms for edge detection with Sobel, Prewitt and Robert first order derivatives using a Shared Memory - Single Instruction Multiple Data (SM - SIMD) parallel architecture. From the experimental results, it is inferred that the proposed parallel algorithms for edge detection are faster than the conventional methods.

I. Introduction

The segmentation can be performed based on existence of either similarity or dissimilarity in the image. It forms different clusters based on the properties such as intensity, color, texture, etc. The result of segmentation process is used in the applications such as biomedical systems for detecting tumors, transportation systems to locate objects such as road, forest, land, etc in the satellite images, object and pattern recognition, biometric for identifying the persons through Iris, finger print, image retrieval, robotic system [16], etc.