Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorit...Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.展开更多
This paper presents a new approach to the outdoor road scene understand-ing by using omni-view images and backpropagation networks. Both the road directions used for vehicle heading and the road categories used for ve...This paper presents a new approach to the outdoor road scene understand-ing by using omni-view images and backpropagation networks. Both the road directions used for vehicle heading and the road categories used for velilcle local-ization are determined by the integrated system. There are three main features about the work. First, an omni-view image sensor is used to extract image samples, and the original image is preprocessed so that the inputs of the net-work is rotation-invariant and simple. Second, the problem of the network size,especially the number of the hidden units, is decided by the analysis of system-atic experimental results. Finally, the internal representation, which reveals the properties of the neural network, is analyzed in the view point of visual signal processing. Experimental results with real scene images are encouraging.展开更多
基金Project supported by the National Basic Research Program (973) of China (Nos. 2004CB318000 and 2002CB312104), the National Natural Science Foundation of China (Nos. 60133020 and 60325208) and the Natural Science Foundation of Beijing (No. 1062006), China
文摘Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.
文摘This paper presents a new approach to the outdoor road scene understand-ing by using omni-view images and backpropagation networks. Both the road directions used for vehicle heading and the road categories used for velilcle local-ization are determined by the integrated system. There are three main features about the work. First, an omni-view image sensor is used to extract image samples, and the original image is preprocessed so that the inputs of the net-work is rotation-invariant and simple. Second, the problem of the network size,especially the number of the hidden units, is decided by the analysis of system-atic experimental results. Finally, the internal representation, which reveals the properties of the neural network, is analyzed in the view point of visual signal processing. Experimental results with real scene images are encouraging.
