Study Foreground Image Segmentation by Narasimhaiah Hari (7 results)
Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG 2011
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Taschenbuch. Condition: Neu. Study Of Foreground Image Segmentation using Support Vector Machines | Supervised Foreground Image Segmentation using Support Vector Machines (SVM) | Hari Prasad Narasimhaiah | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639366624 | Verantwortliche Person für die EU: preigu GmbH & Co. KG…, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG 2011
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Condition: New. Print on Demand pp. 48 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.
Language: English
Published by VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG 2011
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Narasimhaiah Hari PrasadHari Prasad holds an B.Tech(Bachelor of Technology) degree in computer science from IIT(Indian Institute of Technology), Madras. Currently he is working…in Finisar Malaysia, which is a global leader in Fiber O.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book gives a detailed investigation of a supervised image segmentation task using Support Vector Machine (SVM). Support vector machine is considered a good candidate because of its good generalization performance, especially when th…e number of training samples is very small and the dimension of feature space is very high. An SVM based system constructs a hyper-plane in a higher or infinite dimensional space, which can be used for classification, regression, and other tasks. At first, features are extracted from the training images and class labels are assigned accordingly. Using those features, the system is trained. On finishing the training process test samples are fed to the system. As an output, the trained system will classify image pixels into one of those two classes and we will get a segmented image consisting of two distinct classes. Experimental results show that Support vector machine is a promising technique in image segmentation, which is compared with another supervised method, GMM (Gaussian Mixture Model).
