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Natural object classification using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Singh, S. Markou, M. Haddon, J.
Copyright Year	2000
Description	Author affiliation: PANN Res., Exeter Univ., UK (Singh, S.)
Abstract	In this paper we apply artificial neural networks for classifying texture data of various natural objects found in FLIR images. Hermite functions are used for texture feature extraction from segmented regions of interest in natural scenes taken as a video sequence. A total of 2680 samples for a total of twelve different classes are used for object recognition. The results on correctly identifying twelve natural objects in scenes are compared across ten folds of the cross-validation study. Neural networks are found to be extremely effective in robust classification of our data giving an average recognition rate of 91.8%.
Starting Page	139
Ending Page	144
File Size	340931
Page Count	6
File Format	PDF
ISBN	0769506194
ISSN	10987576
DOI	10.1109/IJCNN.2000.861294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Image segmentation Layout Object recognition Image analysis Feature extraction Neural networks Robustness Performance analysis Image texture analysis
Content Type	Text
Resource Type	Article

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