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Human Face Recognition Using Third-Order Synthetic Neural Networks
Uwechue, Okechukwu A.; Pandya, Abhijit
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Description for Human Face Recognition Using Third-Order Synthetic Neural Networks
paperback. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 138 pages, biography. BIC Classification: PHS; UG; UYQV; UYT. Category: (G) General (US: Trade). Dimension: 235 x 155 x 8. Weight in Grams: 231.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2012
Publisher
Springer United States
Number of pages
138
Condition
New
Series
The Springer International Series in Engineering and Computer Science
Number of Pages
123
Place of Publication
New York, NY, United States
ISBN
9781461368328
SKU
V9781461368328
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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