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https://doi.org/10.1016/0262-8856(95)01065-3
Title: | Invariant object recognition using a neural template classifier | Authors: | Tang, H.W. Srinivasan, V. Ong, S.H. |
Keywords: | Invariance Neural networks Object recognition Template classifier |
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. https://doi.org/10.1016/0262-8856(95)01065-3 | 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/10635/80628 | ISSN: | 02628856 | DOI: | 10.1016/0262-8856(95)01065-3 |
Appears in Collections: | Staff Publications |
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