Abstract
A neural network model of visual pattern recognition called the “neocognitron,” was earlier proposed by the author. It is capable of deformation-invariant visual pattern recognition. After learning, it can recognize input patterns without being affected by deformation, changes in size, or shifts in position. This paper offers a mathematical analysis of the process of visual pattern recognition by the neocognitron. The neocognitron is a hierarchical multilayered network. Its initial stage is an input layer, and each succeeding stage has a layer of “S-cells” followed by a layer of “C-cells.” Thus, in the whole network, layers of S-cells and C-cells are arranged alternately. The process of feature extraction by an S-cell is analyzed mathematically in this paper, and the role of the C-cells in deformation-invariant pattern recognition is discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
