Proceedings Article | 16 June 1995
KEYWORDS: Continuous wavelet transforms, Wavelets, Anisotropy, Wavelet transforms, Optical character recognition, Detection and tracking algorithms, 3D image processing, Signal detection, Image processing, Visualization
Both in 1D (signal analysis) and 2D (image processing), the wavelet transform (WT) has become by now a standard tool. Although the discrete version, based on multiresolution analysis, is probably better known, the continous WT (CWT) plays a crucial role for the detection and analysis of particular features in a signal, and we will focus here on the latter. In 2D however, one faces a practical problem. Indeed, the full parameter space of the wavelet transform of an image is 4D. It yields a representation of the image in position parameters (range and perception angle), as well as scale and anisotropy angle. The real challenge is to compute and visualize the full continuous wavelet transform in all four variables--obviously a demanding task. Thus, in order to obtain a manageable tool, some of the variables must be frozen. In other words, one must limit oneself to sections of the parameter space, usually 2D or 3D. For 2D sections, two variables are fixed and the transform is viewed as a function of the two remaing ones, and similarly for 3D sections. Among the six possible 2D sections, two play a privileged role. They yield respectively the position representation, which is the standard one, and the scale-angle representation, which has been proposed and studied systematically by two of us in a number of works. In this paper we will review these results and investigate the four remaining 2D representations. We will also make some comments on possible applications of 3D sections. The most spectacular property of the CWT is its ability at detecting discontinuities in a signal. In an image, this means in particular the sharp boundary between two regions of different luminosity, that is, a contour or an edge. Even more prominent in the transform are the corners of a given contour, for instance the contour of a letter. In a second part, we will exploit this property of the CWT and describe how one may design an algorithm for automatic character recognition (here we obviously work in the position--range-perception angle--representation). Several examples will be exhibited, illustrating in particluar the robustness of the method in the presence of noise.
