Research and Development

Department of Computer Science and Engineering

List of Guides and their Research Specialization

Guide Name Specialization
Dr. B.K Raghavendra Big Data and Image Processing
Dr. M T Gopalakrishna Image Processing
Dr.Raghavendra S Data Mining

List of Faculty Members Registered for Doctorate Program and their Research Areas

Sl No Faculty Name Research Area University/Registered Year
1 Mr. GOVINDARAJU G N Big Data VTU/2014
3 Mr. SANTOSH KUMAR J Big Data VTU/2015
4 Mr. SANDEEP H VTU/2016
6 Mr.DEEPAK M D VTU/2016
7 Mrs. PREETHI.P Image Processing VTU/2016
8 Mr. ASHOKA S VTU/2017
9 Mrs. SHRUTHI U VTU/2017

Funded Projects

Sl No Name of the Granting Authority Name of the Faculty Name of the Student/Team lead Grant Amount Title Status
1 KSCST MR. GOVINDARAJU GN MR. AKHILESH R 3,000 Analysis of Diabetic Patient Medical Record To Predict The Severity Of Risk Involved For The Patient Using Hadoop Ongoing

  • Research topics by Dr. B. K Raghavendra 


    Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze and machine Learning is a subject of artificial intelligence concerned with techniques that allows computer to improve their outputs based on previous experiments. Big data analytics is the process of examining large amounts of data of a variety of types (big data) to discover hidden patterns, unknown correlations and other useful information. Map reduce is the architecture of big data processing system where data processing takes places to analyze the BigData processing MapReduce Techniques and propose a framework in MapReduce to process large, unstructured and diverse data sets to increase the efficiency of the Big data processing and dynamicity.


    In the past, new technology has become more progressed and less expensive. With the availability of high speed processors and inexpensive webcams, more and more people have become interested in real-time applications that involve image processing.HCI is the one of research area in the field of Image and Video Processing. It aims to study of how people design, implement, and use interactive computer systems and how computers affect individuals, organizations, and society. This encompasses not only ease of use but also new interaction (e.g. face, hands)  techniques for supporting user tasks, providing better access to information and creating more powerful forms of communication. The current evaluation of computer technologies has enhanced various applications in human-computer interface. Hand, Face and gesture recognition is a part of this field, which can be applied in various application such as in robotic, security system, driver’s monitor.

    The current progression of computer technology has established many different applications in human computer interface. Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. Also Features of Face detection from video is a very active research area in the computer application. Face and gesture recognition is still remains a challenging and difficult problem to enable a computer to deal with face detection. Current research is  to develop a new algorithm to communicate with computer using various features of human face.

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  • Current research topics by Dr. M T Gopalakrishna

         1. Background Model for Foreground Detection in Real-World Dynamic Scenes.

    The 21st century, public safety and security have become an important issue that concerns governments throughout the world. Surveillance through video cameras is an important aspect in this regard. A human observer of the video can perform surveillance with reasonable accuracy due to his/her excellent visual processing capabilities. However, it becomes cumbersome and impractical for humans, when uninterrupted surveillance is required to be carried out at numerous places. Therefore, automatic systems are required that effectively employ methods and rules in order to perform video surveillance. Emulation of human visual processing capabilities of an automatic system is an extremely challenging problem. A typical automatic video surveillance system performs many video analysis tasks such as reconstruction, camera calibration, registration, compression, indexing, denoising, enhancement, motion estimation, segmentation, object tracking, and mining. The target of this research is to solve the various tasks associated with automatic video surveillance systems while introducing traditional as well as emerging computational methods for analysis of videos.

    2. Object Tracking under Occlusion Using Multiple Cameras

     Video object tracking is an essential component of the video surveillance system. Object tracking refers to the task of tracking individual moving objects accurately from one frame to another in an image sequence. This process typically involves matching objects in consecutive frames using features such as line segments, geometrical points and blob appearance. It is the essential and the most difficult part in video surveillance as occlusions would interrupt normal object tracking process and make it more complex. Many of these applications of tracking have the goal to have ability to automatically understand events happening at a site. The higher level understanding of events requires certain lower level tasks to be performed. These may include tracking objects, handling occlusion and detection of unusual motion.

    3.      Enhancement of Poor Quality Mobile Video Sequences.

    The main problem addressed in this research work is to enhancement the mobile video sequences using efficient algorithms based on new enhancement approaches such as fuzzy logic, artificial intelligence, wavelet transforms and nature inspired intelligent techniques. The proposed research involves exploring, analyzing and formulating novel video image enhancement schemes suitable for various environmental conditions. More precisely, proposed research will focus on the enhancement of videos captured under poor illumination conditions, foggy situations and noisy environment. Most of the video in the aforementioned category are hard to analyze by humans due to the low contrast. Through relevance feedback from domain experts in areas such as surveillance and tracking and through using intelligent and evolutionary learning algorithms, semi-automatic systems that will help analyze given problem need to be developed.

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