Title :
Object Identification with Uncertain Information using Fuzzy Classification
Author :
Jayasiri, Awantha ; Jayasekara, Buddhika ; Udawatta, Lanka
Author_Institution :
Dept. of Electr. Eng., Univ. of Moratuwa
Abstract :
Object identification in unknown environment when uncertain information is presented is a challenging research area. A sensory fusion technique where the image and distance information are fused to produce better results is discussed in this paper. Two types of low cost sensors used for collect the image and distance information. Distance information is filtered through fuzzy filtering to reduce the noise while the two dimensional fast Fourier transform was taken for image information in certain grid points. This information clustered through unsupervised learning technique such as fuzzy C-means clustering and extract cluster centers. Then the training data set is constructed accordingly. This information is used to train a back propagation type neural network. After training the neural network it was tested with the testing data. The results show successful accuracy and performance when using this unsupervised generic input vector construction method
Keywords :
backpropagation; fast Fourier transforms; filtering theory; fuzzy neural nets; image classification; image fusion; object detection; unsupervised learning; back propagation; cost; fast Fourier transform; learning technique; neural network; object identification; sensory fusion technique; uncertain information; Costs; Data mining; Fast Fourier transforms; Image sensors; Information filtering; Information filters; Neural networks; Noise reduction; Testing; Unsupervised learning; Fuzzy C-means clustering; Fuzzy filtering; Neural networks; Sensory fusion;
Conference_Titel :
Industrial and Information Systems, First International Conference on
Conference_Location :
Peradeniya
Print_ISBN :
1-4244-0322-7
DOI :
10.1109/ICIIS.2006.365791
