摘要
Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.
Many methods have been proposed to extract the most relevant areas of an image. This article explores the method of edge detection by the multiscale product （MP） of the wavelet transform. The wavelet used in this work is the first derivative of a bidimensional Gaussian function. InitiaRy, we construct the wavelet, then we present the MP approach which is applied to binary and grey levels images. This method is compared with other methods without noise and in the presence of noise. The experiment results show fhht the MP method for edge detection outPerforms conventional methods even in noisy environments.
基金
supported by the University of Tunis El Manar and National Engineering School of Tunis
