Isbn: 9783330034747 - sift & surf on gpus for feature extraction: analysis on wang dataset for cbir (6 risultati)

Paperback. Condizione: Brand New. 72 pages. 8.66x5.91x0.17 inches. In Stock.

Taschenbuch. Condizione: Neu. SIFT & SURF on GPUs for Feature Extraction | Analysis on WANG Dataset for CBIR | Bhavneet Kaur (u. a.) | Taschenbuch | 72 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330034747 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -CBIR is required since large image databases are used both in scientific and medical domains and in the vast advertising/marketing domain.Since, images are a complex data to handle as they are composed of matrices and vectors of data and also due to multithread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. GPU is a powerful graphics engine and a highly parallel programmable processor having better efficiency and high speed that overshadows CPU. A Content Based Image Retrieval system is implemented and tested on benchmark WANG database using suitable and efficient techniques so as to achieve efficient results. The feature extraction phase is parallelized on GPU processor to obtain the speed up. Sequential and parallel implementation of CBIR is done using OpenCV. SURF and SIFT algorithms when implemented on GPU show better performance for feature extraction and for retrieval of images. The execution time is noted and on comparison with its corresponding serial counterpart a considerable speed up has been achieved. 72 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - CBIR is required since large image databases are used both in scientific and medical domains and in the vast advertising/marketing domain.Since, images are a complex data to handle as they are composed of matrices and vectors of data and also due to multithread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. GPU is a powerful graphics engine and a highly parallel programmable processor having better efficiency and high speed that overshadows CPU. A Content Based Image Retrieval system is implemented and tested on benchmark WANG database using suitable and efficient techniques so as to achieve efficient results. The feature extraction phase is parallelized on GPU processor to obtain the speed up. Sequential and parallel implementation of CBIR is done using OpenCV. SURF and SIFT algorithms when implemented on GPU show better performance for feature extraction and for retrieval of images. The execution time is noted and on comparison with its corresponding serial counterpart a considerable speed up has been achieved.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur BhavneetAuthors are working as Assistant Professor at Department of CSE, Shaheed Bhagat Singh State Technical Campus, Ferozepur, India. Both the authors have published several research papers in reputed International/National Jo.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -CBIR is required since large image databases are used both in scientific and medical domains and in the vast advertising/marketing domain.Since, images are a complex data to handle as they are composed of matrices and vectors of data and also due to multithread execution of algorithms, programmability and low cost, image processing becomes an appropriate field of achieving parallelism. GPU is a powerful graphics engine and a highly parallel programmable processor having better efficiency and high speed that overshadows CPU. A Content Based Image Retrieval system is implemented and tested on benchmark WANG database using suitable and efficient techniques so as to achieve efficient results. The feature extraction phase is parallelized on GPU processor to obtain the speed up. Sequential and parallel implementation of CBIR is done using OpenCV. SURF and SIFT algorithms when implemented on GPU show better performance for feature extraction and for retrieval of images. The execution time is noted and on comparison with its corresponding serial counterpart a considerable speed up has been achieved.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.…