Prabhakar C.J.

Work place: Dept. of P.G Studies and Research in Computer Science, Kuvempu University, Shankaraghatta-577451, Shimoga, Karnataka, India



Research Interests: Computer Graphics and Visualization, Pattern Recognition, Computer Vision, Computational Learning Theory, Computer systems and computational processes


Prabhakar C.J. received his Ph.D. degree in Computer Science and Technology from Gulbarga University, Gulbarga, Karnataka, India, in 2009. He is currently working as Assistant Professor in the department of Computer Science and M.C.A, Kuvempu University, Karnataka, India. His research interests are pattern recognition, computer vision, machine learning and video processing.

Author Articles
Extraction of Scene Text Information from Video

By Too Kipyego Boaz Prabhakar C.J.

DOI:, Pub. Date: 8 Jan. 2016

In this paper, we present an approach for scene text extraction from natural scene video frames. We assumed that the planar surface contains text information in the natural scene, based on this assumption, we detect planar surface within the disparity map obtained from a pair of video frames using stereo vision technique. It is followed by extraction of planar surface using Markov Random Field (MRF) with Graph cuts algorithm where planar surface is segmented from other regions. The text information is extracted from reduced reference i.e. extracted planar surface through filtering using Fourier-Laplacian algorithm. The experiments are carried out using our dataset and the experimental results indicate outstanding improvement in areas with complex background where conventional methods fail.

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Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques

By Mohana S.H. Prabhakar C.J.

DOI:, Pub. Date: 8 Aug. 2015

In this paper, we present computer vision based technique to detect surface defects of citrus fruits. The method begins with background removal using k-means clustering technique. Mean shift segmentation is used for fruit region segmentation. The candidate defects are detected using threshold based segmentation. In this stage, it is very difficult to differentiate stem-end from actual defects due to similarity in appearance. Therefore, we proposed a novel technique to differentiate stem-end from actual defects based on the shape features. We conducted experiments on our citrus data set captured in controlled environment. The experiment results demonstrate that our technique outperforms the existing techniques.

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