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Image Registration for Recovering Affine Transformation Using Nelder Mead Simplex Method for Optimization
Mehfuza Suleman Holia, V.K.Thakar
Pages - 218 - 228     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Vector Quantization, Multi-core, Shared Memory, Clustering
This paper proposes a parallel approach for the Vector Quantization (VQ) problem in image processing. VQ deals with codebook generation from the input training data set and replacement of any arbitrary data with the nearest codevector. Most of the efforts in VQ have been directed towards designing parallel search algorithms for the codebook, and little has hitherto been done in evolving a parallelized procedure to obtain an optimum codebook. This parallel algorithm addresses the problem of designing an optimum codebook using the traditional LBG type of vector quantization algorithm for shared memory systems and for the efficient usage of parallel processors. Using the codebook formed from a training set, any arbitrary input data is replaced with the nearest codevector from the codebook. The effectiveness of the proposed algorithm is indicated.
CITED BY (14)  
1 Madhuri, G. S., & Indra Gandhi, M. P. (2015, April). Image registration quality assessment with similarity measures-A research study. In Communications and Signal Processing (ICCSP), 2015 International Conference on (pp. 0084-0088). IEEE.
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3 Ansari, S. M., & Kale, K. V. (2014). Mapping and analysis of crime in Aurangabad city using GIS. IOSR-Journal of Computer Engineering, July-Aug, 16(4), 67-76.
4 Sperling, D. (2014). MRI-ultrasound fusion imaging. In Image Guided Prostate Cancer Treatments (pp. 115-123). Springer Berlin Heidelberg.
5 Hazra, J., Chowdhury, A. R., & Dutta, P. (2013). An approach for determining angle of rotation of a gray image using weighted statistical regression. International Journal of Scientific and Engineering Research, 1006.
6 Machálek, T., & Olševicová, K. (2013). Decentralized multi-agent algorithm for translational 2d image alignment. In Multimedia and Internet Systems: Theory and Practice (pp. 15-24). Springer Berlin Heidelberg.
7 Guo Jia, Li, Wang, Li Si & congregation. (2013). An image registration algorithm based on mutual information. Sensing Technology, 26 (7), 958-960.
8 Dutta, P. K., Mishra, O. P., & Naskar, M. K. (2013). A Method for Post-hazard Assessment Through Topography Analysis using Regional Segmentation for Multi-temporal Satellite Imagery: A Case Study of 2011 Tohuku Earthquake Region. International Journal of Image, Graphics and Signal Processing, 5(10), 63.
9 Athavale, N. P., & Talele, K. T. (2013). Comparison of various Image Registration Techniques with the Proposed Hybrid System.
10 Machálek, T. Vyuziti metody Particle Swarm Optimization pri vzájemném zarovnáváni 2D obrázku Diplomová práce.
11 Ahmed, M. M. (2012). Two-Dimensional Block of Spatial Convolution Algorithm and Simulation. International Journal of Image Processing (IJIP), 6(4), 243.
12 Bhowmik, M. K., De, B. K., Bhattacharjee, D., Basu, D. K., & Nasipuri, M. (2012, May). Multisensor fusion of visual and thermal images for human face identification using different SVM kernels. In Systems, Applications and Technology Conference (LISAT), 2012 IEEE Long Island (pp. 1-7). IEEE.
13 Chadha, A., Jyoti, D., & Roja, M. M. (2011). Rotation, Scaling and Translation Analysis of Biometric Signature Templates. arXiv preprint arXiv:1110.1208.
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Miss Mehfuza Suleman Holia
- India
Dr. V.K.Thakar
- India