Feature Extraction Based on Wavelet Moments and Moment Invariants in Machine Vision Systems
George A. Papakostas, Dimitrios E. Koulouriotis, Vassilios D. Tourassis · InTech eBooks · 2012
Recently, there has been an increasing interest on modern machine vision systems for industrial and commercial purposes.More and more products are introduced in the market, which are making use of visual information captured by a camera in order to perform a specific task.Such machine vision systems are used for detecting and/or recognizing a face in an unconstrained environment for security purposes, for analysing the emotional states of a human by processing his facial expressions or for providing a vision based interface in the context of the human computer interaction (HCI) etc..In almost all the modern machine vision systems there is a common processing procedure called feature extraction, dealing with the appropriate representation of the visual information.This task has two main objectives simultaneously, the compact description of the useful information by a set of numbers (features), by keeping the dimension as low as possible.Image moments constitute an important feature extraction method (FEM) which generates high discriminative features, able to capture the particular characteristics of the described pattern, which distinguish it among similar or totally different objects.Their ability to fully describe an image by encoding its contents in a compact way makes them suitable for many disciplines of the engineering life, such as image analysis (Sim et al., 2004), image watermarking (Papakostas et al., 2010a) and pattern recognition (Papakostas et al., 2007(Papakostas et al., , 2009a(Papakostas et al., , 2010b)).Among the several moment families introduced in the past, the orthogonal moments are the most popular moments widely used in many applications, owing to their orthogonality property that comes from the nature of the polynomials used as kernel functions, which they constitute an orthogonal base.As a result, the orthogonal moments have minimum information redundancy meaning that different moment orders describe different parts of the image.In order to use the moments to classify visual objects, they have to ensure high recognition rates for all possible object's orientations.This requirement constitutes a significant operational feature of each modern pattern recognition system and it can be satisfied during www.intechopen.com