Recognition of 2D objects from the wavelet transform zero-crossing representation

Wavelet theory provides very general techniques that can be utilized to perform many tasks in signal and image processing applications. The zero-crossings of a wavelet transform of a signal, using a particular class of wavelets, provide the locations of the sharp variation points of the signal at the different resolutions. These points provide meaningful features for characterizing the signals. In this paper, we present a new approach to recognize a 2D object of general shape based on its wavelet transform zero-crossing representation. This is performed in two stages. The first stage consists of building a 1D signal representation of the 2D boundary of the object followed by obtaining the zero-crossing of the wavelet transform of the resulting representation. The second stage is the matching procedure for object recognition. Our algorithm uses only a few intermediate resolution levels for matching thus making it computationally efficient while being less sensitive to noise and quantization errors. A normalization process is implemented for matching objects of different scales to their models both in noisy and noise-free situations. Our algorithms have been tested using simulated object boundaries and have been successful in recognizing the objects with results being invariant under translation, rotation and scaling.