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|Title:||Invariant object recognition using a neural template classifier||Authors:||Tang, H.W.
|Issue Date:||Jul-1996||Citation:||Tang, H.W., Srinivasan, V., Ong, S.H. (1996-07). Invariant object recognition using a neural template classifier. Image and Vision Computing 14 (7) : 473-483. ScholarBank@NUS Repository.||Abstract:||This paper describes an efficient two-stage neural network for invariant object recognition. It consists of a feature extractor trained by an ART-like fast saturation learning scheme and a delta-rule trained classifier. Objects, represented as edge strength maps derived from raw input images, are scaled to a normalized size and rotated in discrete steps to generate a sequence of localized input feature vectors. The network outputs identify the object and permit the calculation of a confidence level. Experiments show that the system works well even when there is noise and occlusion.||Source Title:||Image and Vision Computing||URI:||http://scholarbank.nus.edu.sg/handle/10635/62351||ISSN:||02628856|
|Appears in Collections:||Staff Publications|
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