M. V. D. Prasad
K L University
12 Papers
49 Citations
M. V. D. Prasad is an academic researcher from K L University. The author has contributed to research in topics: Computer science & Sign language. The author has an hindex of 6, co-authored 10 publications. Previous affiliations of M. V. D. Prasad include University of Fiji.
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Papers
4-Camera model for sign language recognition using elliptical fourier descriptors and ANN
P.V.V. Kishore,M. V. D. Prasad,Ch. Raghava Prasad,R. Rahul +3 more
- 12 Mar 2015
TL;DR: A 4 camera model for recognizing gestures of Indian sign language using artificial neural networks with backpropagation training algorithm is proposed and the classification rate is computed which provides experimental evidence that4 camera model outperforms single camera model.
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Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks
P. V. V. Kishore,K. V. V. Kumar,E. Kiran Kumar,A. S. C. S. Sastry,M. Teja Kiran,D. Anil Kumar,M. V. D. Prasad +6 more
- 22 Jan 2018
TL;DR: This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN), and achieves a 93.33% recognition rate compared to other classifiers reported on the same dataset.
Optical Flow Hand Tracking and Active Contour Hand Shape Features for Continuous Sign Language Recognition with Artificial Neural Networks
P.V.V. Kishore,M. V. D. Prasad,D. Anil Kumar,A. S. C. S. Sastry +3 more
- 01 Feb 2016
TL;DR: To extract hand tracks and hand shape features from continuous sign language videos for gesture classification using backpropagation neural network, Horn Schunck optical flow (HSOF) extracts tracking features and Active Contours (AC) extract shape features.
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An efficient classification of flower images with convolutional neural networks
M. V. D. Prasad,B JwalaLakshmamma,A Hari Chandana,K Komali,M V.N. Manoja,P. Rajesh Kumar,Ch Raghava Prasad,Syed Inthiyaz,P Sasi Kiran +8 more
TL;DR: This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN), and achieves 97.78% recognition rate compared to other classifier models reported on the same dataset.
Denoising Ultrasound Medical Images with Selective Fusion in Wavelet Domain
TL;DR: Visual quality through twofold processing has improved to an interesting level and the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST).
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