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.