A skeleton parallel algorithm 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 a Replicated Shared Object Model. This is a generic program at the design level at which neither data types nor the code of specific data-dependent procedures need to be detailed. Multi-Scale Image Theory plays art important role in this research. The Spiral Architecture is fundamental to this approach. Our work provides an efficient algorithm for edge detection and is robust to noise.