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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/846

Title: Practical, Computation Efficient High-Order Neural Network for Rotation and Shift Invariant Pattern Recognition
Authors: Artyomov, Evgeny
Yadid-Pecht, Orly
Keywords: HONN
Higher-Order Networks
Invariant Pattern Recognition
Issue Date: 2004
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: In this paper, a modification for the high-order neural network (HONN) is presented. Third order networks are considered for achieving translation, rotation and scale invariant pattern recognition. They require however much storage and computation power for the task. The proposed modified HONN takes into account a priori knowledge of the binary patterns that have to be learned, achieving significant gain in computation time and memory requirements. This modification enables the efficient computation of HONNs for image fields of greater that 100 × 100 pixels without any loss of pattern information.
URI: http://hdl.handle.net/10525/846
ISSN: 1313-0463
Appears in Collections:Volume 11 Number 1

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