Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Roadside accidents are one of the leading cause of deaths in India. Though car accidents are preventable, number of causalities due to car accidents have only seen an increase over the years. This paper looks at one of the promising solutions to this problem – autonomous cars. While self-driving cars dominating the Indian roads is still a distant future, it is important to understand and improve the image preprocessing required in a self-driving car before they are omnipresent in our society. This paper interprets the released government data on roadside accidents in India and studies different types of noise with varying intensity to help choose the most appropriate smoothing filter in spatial domain. The paper then focuses on another important aspect of pre-processing, edge detection and compare the prevalent edge-detection techniques with one another. © 2020 IJSTR.

World University root node

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

International Journal of Scientific and Technology Research

Roadside accidents in India and image preprocessing for autonomous cars

Temporal video segmentation is the primary step of content based video retrieval. The whole processes of video management are coming under the focus of content based video retrieval, which includes, video indexing, video retrieval, and video summarization etc. In this paper, we proposed a computationally efficient and discriminating shot boundary detection method, which uses a local feature descriptor named local Contrast and Ordering (LCO) for feature extraction. The results of the experiments, which are conducted on the video dataset TRECVid, analyzed and compared with some existing shot boundary detection methods. The proposed method has given a promising result, even in the cases of illumination changes, rotated images etc.

International Journal of Engineering and Advanced Technology Regular Issue

An Efficient and Robust Temporal Video Segmentation

Plentiful electronic gadgets for video capturing and processing attract a large number of applications using videos as their inputs. They analyze videos to gather information for their business. SBD aims to detect transition and their boundaries between consecutive shots; hence, shots with rich information are used in the content-based video indexing and retrieval. Most of the short boundary detection algorithms suffer from a large number of false positives missed and cuts. In this paper, a hybrid approach is used to reduce the false positives and ensure high coverage of cuts in the abrupt transition detection. Experiment results show that this hybrid approach has higher accuracy than individual methods such as GLCM, grey histogram and SIFT matching. © 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.

Journal of Advanced Research in Dynamical and Control Systems

Abrupt transition detection using grey scale features

Electronic Commerce applications support the interaction between various parties participating in a commerce transaction as well as the management of the data involved in the process via the network or the internet. Electronic commerce is gaining traction as an acknowledged and used business model. More and more corporates are implementing web sites providing functionality for performing commercial transactions over the internet to better reach customers as it is sensible to say that the process of shopping on the web is becoming common. In this article we intend to discuss the background for developing an electronic commerce application, principles and methods involved in developing an ecommerce application. © 2017 IEEE.

VIT Unitversity

2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)

Developing an electronic commerce platform

In Scientific field, the data collection resulted into large scale growth by continuous additions of data. The characteristics such as velocity, variety, volume, etc. make the collected data as Big data. Analysis of such data uses various data mining techniques. Clustering is one among them and it is an unsupervised learning technique used for statistical data in bioinformatics, social networks, etc. Among various clustering techniques the K -means clustering is a common and predominant algorithm. However, the accuracy of original K-means algorithm heavily depends on centroids at the beginning and it has high computational complexity. In this paper we present an empirical study on enhanced k-means algorithm for high accuracy clustering with the initial centroids selection in an improved manner. © Research India Publications.

Department of Software Systems

Department of Computational Intelligence

International Journal of Applied Engineering Research

Case study on enhanced K-means algorithm for bioinformatics data clustering

Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hard problem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. This project attempts to examine those techniques, and to apply them to build a simple voice recognition system. The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), Vector Quantization (VQ) which are implemented using MATLAB. MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. VQ codebook is generated by clustering the training feature vectors of each speaker and then stored in the speaker database. Verification of the speaker is carried out using Euclidian Distance. For voice recognition we implement the MFCC approach using software platform MatlabR2013b. © Research India Publications.