Edge detection: wavelets versus conventional methods on DSP processors

Abstract

Edge detection is a cornerstone in any computer, robotic or machine vision system. Real time edge detection is a pre-process to many critical applications, such as assembly line inspection and surveillance. Wavelets-based algorithms are replacing traditional algorithms, especially the Haar wavelet because of its simplicity. The Haar algorithm uses a multilevel decomposition to produce image edges corresponding to high frequency wavelet coefficients. In this paper, a real time edge detection algorithm based on Haar is analyzed and compared to conventional edge detectors. Other implemented and compared algorithms are the traditional Prewitt algorithm, and, from a newer generation, the Canny algorithm. The real time implementation of all algorithms is accomplished using TI TMS320C6711 card. In case of Haar, the multilevel decomposition improves the results obtained with noisy images. The results show that the Haar-based edge detector has a low execution time with accurate edge results, and thus represents a suitable algorithm for on-line vision system applications. Canny has produced the thinnest edges, but is not suitable for real time processing using the 6711, and falls short in edge results compared to the Haar results. The wavelet-based algorithm has outperformed other edge detectors.