Detail The Race to The Attractor Model for Classifying Objects   Oleh:  Ferland, G. J. M. G. ; Yeap, T. H. Jenis: Article from Bulletin/Magazine Dalam koleksi: IEEE Instrumentation & Measurement Magazine vol. 3 no. 4 (2000), page 18-23. Topik: OBJECTS; race; attractor model; classifying objects Ketersediaan Perpustakaan Pusat (Semanggi) Nomor Panggil: II47.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada     Lihat Detail Induk Isi artikel The human brain is exceptionally good at classifying objects quickly and reliably. We can recognize familiar faces even when seen from different angles, despite irrelevant clutter such as jewellery, sunglasses, new hair styles, etc. Over the years, scientists have tried to duplicate this remarkable ability using neural networks models, but without much success. In this article, we examine some of the key characteristics that make the brain such an efficient tool for object recognition. We propose mechanisms through which these characteristics can be modeled. Then, we describe a novel approach to simulating object recognition with artificial neural networks. We used the recognition process for "Uncle Brian" to demonstrate how this model captures these key characteristics and achieves reliable classification. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Kembali

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