Object Matching using Skeletonization based on Hamming Distance

Abstract

Identification of an object from a dynamic background is a challenging process in computer vision and pattern matching research. The proposed algorithm identifies moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy environment. In connection with our previous work, here we have proposed a methodology to perform skeletanization of an object and identifies it. The recent vehicle recognition methods could fail to recognize an object and produce more false acceptance rate(FAR) or false rejection rate (FRR). This paper recommends a method for object identification using weighted distance to extract features. Experimental results and comparisons using real data demonstrate the pre-eminence of the proposed approach.

Translation of Brahmi Inscriptions to Sinhala using Natural Language Processing

“…After performing one to one mapping, system generates the transliterated Sinhala string of the particular Brahmi string. The developed functionality is very less time consuming, high accurate and efficient [4].…”