Pattern recognition using vector quantization augmented with moment-based feature vectors

The need for data compression exists primarily in data transmission and storage of information. Data compression techniques try to minimise the cost involved and sometimes try to reduce the bandwidth of the digital signal below its analog bandwidth requirements. The theory of moments provides an interesting and sometimes useful alternative to series expansions for representing shape of objects. In this paper, certain functions of moments, which are invariants to geometric transformations, are discussed and how features are useful in identification and classification of objects with unique shapes regardless of their location, size and orientation with the concepts of vector quantization.