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Titlebook: Recent Trends in Image Processing and Pattern Recognition; First International K.C. Santosh,Mallikarjun Hangarge,Atul Negi Conference proc

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楼主: Racket
发表于 2025-3-28 16:01:53 | 显示全部楼层
Unlocking Textual Content from Historical Maps - Potentials and Applications, Trends, and Outlookss for their research and using the map content in their studies. These challenges present a tremendous collaboration opportunity for the image processing and pattern recognition community to build advance map processing technologies for transforming the natural and social science studies that use hi
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A Fast k-Nearest Neighbor Classifier Using Unsupervised Clusteringsting property discovered by altering the different selection criteria, we proved the efficiency of the method by reducing by up to 71% the classification speed, while keeping the classification performance in the same range.
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Parshuram M. Kamble,Ravindra S. Hegadihat motivated expeditions with their focus on natural history, geology, ichthyology, botany, zoology, helminthology, speleology, physical anthropology, oceanography, meteorology and magnetism.  . . .978-1-349-84534-7978-1-137-58106-8Series ISSN 2730-972X Series E-ISSN 2730-9738
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Eigen Value Based Features for Offline Handwritten Signature Verification Using Neural Network Approputed. The difference and ratios of high and low points of both the envelopes are computed. Lastly average values of both the envelopes are obtained. These features set are coupled with neural network pattern recognition classifier that lead to 98.1% of accuracy and FAR 1.9%.
发表于 2025-3-29 15:56:24 | 显示全部楼层
Combination of Feature Selection Methods for the Effective Classification of Microarray Gene Expression of feature selection techniques. Experimental results suggest that appropriate combination of filter gene selection methods is more effective than individual techniques for microarray data classification. We have compared our combination methods using different learning algorithms.
发表于 2025-3-29 19:50:02 | 显示全部楼层
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabularyandidate regions and for a query symbol is constructed based on the sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed by comparing the similarity between vector models. The first evaluation on SESYD database demonstrates that the proposed method is promising.
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Conference proceedings 2017P2R 2016, held in Bidar, Karnataka, India, in December 2016. .The 39 revised full papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in topical sections on document analysis; pattern analysis and machine learning; image analysis; biomedical image anal
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