A skeleton algorithm on clusters for image edge detection
Image edge detection in computer vision and image processing is a process which detects one kind of significant feature in an image that appears as large delta values in intensities. In this paper, a parallel algorithmic skeleton for edge detection is proposed based on the Spiral Architecture and the Gaussian multi-scale theory. UNIX-based network programming mechanisms in C are used for the implementation on a cluster of Sun-workstations. Our work provides an efficient algorithm for edge detection and is robust to noise.