Optimized Performance and Utilization Analysis of Real-Time Multi Spectral Data/Image Categorization Algorithms for Computer Vision Applications

Abstract

In the field of computer vision , the process of  acquiring, processing,   analyzing   and   understanding  multispectral data  images is a major requisite. The major tool for digital data analysis and object recognition is data categorization. The basic and main stages involved in data categorization are  the  identification  of  an  appropriate  categorization  system,  an  assortment  of training   and   testing   samples   and   the   categorization   method.  Data categorization (or) classification is to recognize and depict the features of any data that can be later used for knowledge discovery. This work aims to compare supervised data classification techniques. This paper  illustrates utilization of various techniques viz., Minimum distance (MD), Maximum likelihood (ML) and Mahalanob is distance (Mad). All the procedures are compared and analyzed for finest results and maximum accuracy.