Titlebook: Human Face Recognition Using Third-Order Synthetic Neural Networks; Okechukwu A. Uwechue,Abhijit S. Pandya Book 1997 Springer Science+Busi

Agoraphobia

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書目名稱Human Face Recognition Using Third-Order Synthetic Neural Networks影響因子(影響力)學科排名




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書目名稱Human Face Recognition Using Third-Order Synthetic Neural Networks被引頻次




書目名稱Human Face Recognition Using Third-Order Synthetic Neural Networks被引頻次學科排名




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書目名稱Human Face Recognition Using Third-Order Synthetic Neural Networks讀者反饋




書目名稱Human Face Recognition Using Third-Order Synthetic Neural Networks讀者反饋學科排名

painkillers

背心

撕裂皮肉

Introduction,ded inspiration to address more practical problems which have . as opposed to . patterns (e.g. alphanumeric characters, aircraft silhouettes) and are also more complex in nature as opposed to purely edge-extracted images - human face recognition . such a problem.

激勵

Implementation of Invariances,. T1 and t1 are similar triangles, T2 and t2 are similar, whereas t1 and t2 are dissimilar. For example, T1 is a scaled and rotated version of t1. T3 is dissimilar to Tl and t1 as it is a scaled, lateral inversion therefore the sequence of internal angles would not be the same. T4 is dissimilar to all of the other triangles.

exophthalmos

Facial Pattern Recognition,nd, unlike Fourier descriptors, do not require closed boundaries. Moment invariants were first proposed by Hu [HU62]_in 1961 using non-linear combinations of regular(geometric) moments which are invariant under scale, translation, and rotation image transformations.

calamity

Overthrow

Network Training, presents input patterns to the network, compares the resulting outputs with those desired, and then adjusts the network weights accordingly in order to reduce the difference. Unsupervised training requires no’ teacher’: input patterns are applied and the network selforganises by updating its weights according to a pre-defined algorithm.

干涉

antenna