Work place: Department of Computer Science, Rani Channamma University, Belagavi, Karnataka.
Research Interests: Data Structures and Algorithms, Image Processing, Image Manipulation, Image Compression, Pattern Recognition
Ashvini K. Babaleshwar completed M.Sc in Computer Science from Rani Channamma University, Belagavi, Karnataka, India in 2013. She is pursuing Ph.D in Computer Science at Rani Channamma University, Belagavi, Karnataka, India. Her area of research includes Image Processing and Pattern recognition, Content based Image and Video retrieval.
DOI: https://doi.org/10.5815/ijigsp.2019.03.06, Pub. Date: 8 Mar. 2019
Content Based Video Retrieval (CBVR) System has been investigated over past decade it’s rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2018.02.06, Pub. Date: 8 Feb. 2018
The Traffic-Sign detection and recognition plays significant role in the design of autonomous driverless cars for navigation purpose as well as to assist a driver for alerting and educating him about the tracked signage on the road side. The main objective of this paper is to highlight an automatic process of detection of Region Of Interest (ROI) which marks or isolates signage’s from color video streams and performs classification of automatically detected signage’s based on support vector machine (SVM) classifiers trained over Local Binary Pattern (LBP) features. The training dataset was captured through 13 mega pixel mobile camera in different illumination and light conditions and due to randomness the data base complexity is very high. The robustness of the proposed system is measured on the bases its of capability of automatic detection and classification of ROI in a given video stream and backed with a comprehensive result analysis presented in this piece of work.[...] Read more.
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