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Human Face Recognition Using Third-Order Synthetic Neural Networks (The Springer International Series in Engineering and Computer Science, 410) - Hardcover
Book 151 of 260: The Springer International Series in Engineering and Computer Science
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Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem.
Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns.
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Human face recognition using third-order synthetic neural networks. The international series in engineering and computer science 410.
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Boston, Kluwer Academic Publisher [1997]., 1997
ISBN 10: 0792399579
ISBN 13: 9780792399575
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Condition: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns. Seller Inventory # 3035359/3
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Human Face Recognition Using Third-Order Synthetic Neural Networks
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ISBN 10: 0792399579
ISBN 13: 9780792399575
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Hardback. Condition: New. 1997 ed. Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. High-order networks require little training data (hence, short training times) and have been used to perform transformation-invariant recognition of relatively simple visual patterns, achieving very high recognition rates. The successful results of these methods provided inspiration to address more practical problems which have grayscale as opposed to binary patterns (e.g., alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition is such a problem. Human Face Recognition Using Third-Order Synthetic Neural Networks serves as an excellent reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns. Seller Inventory # LU-9780792399575
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Human Face Recognition Using Third-Order Synthetic Neural Networks
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ISBN 10: 0792399579
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Condition: New. Explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns. This book serves as a reference for researchers and professionals working on applying neural network technology to the recognition of complex visual patterns. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 138 pages, biography. BIC Classification: UG; UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 9. Weight in Grams: 840. . 1997. Hardback. . . . . Seller Inventory # V9780792399575
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