Natural object classification using artificial neural networks

被引：5
|
作者：
Singh, S [1 ]
Markou, M [1 ]
Haddon, J [1 ]
机构：
[1] Univ Exeter, Dept Comp Sci, Pann Res, Exeter, Devon, England
来源：
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL III | 2000年
关键词：
D O I：
10.1109/IJCNN.2000.861294
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 front 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%.
引用
收藏
页码：139 / 144
页数：6
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