A Wavelet Based Method for Affine Invariant 2 D Object Recognition

Recognizing objects that have undergone certain viewing transformations is an important problem in the field of computer vision. Most current research has focused almost exclusively on single aspects of the problem, concentrating on a few geometric transformations and distortions. Probably, the most important one is the affine transformation which may be considered as an approximation to perspective transformation. Many algorithms were developed for this purpose. Most popular ones are Fourier descriptors and moment based methods. Another powerful tool to recognize affine transformed objects, is the invariants of implicit polynomials. These three methods are usually called as traditional methods. Wavelet-based affine invariant functions are recent contributions to the solution of the problem. This method is better at recognition and more robust to noise compared to other methods. These functions mostly rely on the object contour and undecimated wavelet transform. In this thesis, a technique is developed to recognize objects undergoing a general affine transformation. Affine invariant functions are used, based on on image projections and high-pass filtered images of objects at projection angles . Decimated Wavelet Transform is used instead of undecimated Wavelet Transform. We compared our method with the an another wavelet based affine invariant function, Khalil-Bayoumi and also with traditional methods.