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1 2005

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Edge detection : wavelets versus conventional methods on DSP processors

Abdel-Qader I. M., Maddix M. E.

Machine Graphics and Vision

2005

Vol. 14, No. 1

83-101

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 images edges corresponding to high frequency wavelet coefficients. In this paper, a real time edges 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 implementation of all algorithms is accomplished using TI TMS320C6711 card. In case of Haar, the multilevel decomposition improves the results obtained with noise images. The results show that the Haar-based edge detector has a low execution time with accurate edges results, and thus represent a suitable algorithm for on-line vision system applications. Canny has produced the thinnest edges, but is not suitable for time processing using the 6711, and falls short in edge results compared to the Haar results. The Wavelet-based algorithm has outperformed other edges detectors.