Invariant object recognition using a neural template classifier
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摘要
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.
论文关键词:Object recognition,Neural networks,Invariance,Template classifier
论文评审过程:Received 15 November 1995, Revised 2 October 1996, Accepted 2 October 1996, Available online 20 February 1999.
论文官网地址:https://doi.org/10.1016/0262-8856(95)01065-3