Please use this identifier to cite or link to this item:
|Title:||Invariant object recognition using a neural template classifier|
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 5, 2017
WEB OF SCIENCETM
checked on Nov 4, 2017
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.