By Sudipta Roy, Nidul Sinha, Asoke Kr. Sen (auth.), Dhinaharan Nagamalai, Eric Renault, Murugan Dhanuskodi (eds.)
This booklet constitutes the refereed complaints of the 1st foreign convention on electronic picture Processing and trend attractiveness, DPPR 2011, held in Tirunelveli, India, in September 2011. The forty eight revised complete papers have been rigorously reviewed and chosen from approximately four hundred submissions. The convention introduced jointly top researchers, engineers and scientists within the area of electronic picture Processing and trend acceptance. The papers hide all theoretical and sensible points of the sector and current new advances and present learn ends up in tracks, specifically: electronic photograph processing and development reputation, and desktop technological know-how, engineering and knowledge technology.
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Additional info for Advances in Digital Image Processing and Information Technology: First International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011, Tirunelveli, Tamil Nadu, India, September 23-25, 2011. Proceedings
However the histogram based approach is only applicable to relatively low dimensional data as the sparse data distribution degrade the reliability of histograms. K. Pugalendhi, M. A. Victoire with the continuous real valued gene expression datasets for estimating the true density function. The proposed method generates a fuzzy equivalence partition matrix (FEPM) by combining the concept of fuzzy and rough set theory. The generated FEPM is then applied to f- information measure to calculate the relevance and redundancy between genes.
7) Extract the license plate using color information. (8) Transmit the extracted license plate image to the authorized remote station. (9) Go to step (1). Fig. 3. Configuration for the speed measurement of a moving vehicle 30 G. Adinarayana et al. 4 Results Fig. 4. RGB Image Fig. 6. Grascale image Fig. 5. Background Image Fig. 7. Binary Image Implementation of LabVIEW Based Intelligent System Fig. 8. LabVIEW front panel Fig. 9. Pixel valuesfor image subtraction 31 32 G. Adinarayana et al. Fig.
Since the number of classifiers is reduced, the testing time is reduced in the SVM-BDT. The testing time of the QSVM-BDT with prefiltering is further reduced, because prefiltering reduces the search space still more. The training time of the QSVMBDT is slightly increased because of the prefiltering operation. Since the training time is a single time operation, it could be neglected. Fig. 5 shows the comparison graph of the three methods the SVM-PWC, SVM-BDT and QSVMBDT. Fig. 5. Comparison graph of the Accuracy rate, training and testing time 4 Conclusion This paper proposes a Quick SVMBDT framework, that performs an efficient and quick retrieval using the SVM based binary decision tree architecture and prefiltering.