In the recent years Wavelets have opened the door to a flow of new ideas and algorithms that have invaded most traditional fortresses of signal processing. They allow to deal with problems of texture recognition such as shift sensitivity,poor directionality and absence of phase information which conventional methods fail to resolve. This book is intended to make the theory of wavelets understandable to beginners.It covers all types of complex wavelets that exploits the Gabor-like properties of the dual-tree complex wavelet transform, shift invariance and six directional subbands at each scale, and uses a feature vector comprising of a variance and an entropy at different scales of each of the directional subbands. Experimental results demonstrate its robustness against noise and a higher classification accuracy than a discrete wavelet transform based classifier.