Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squ...
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Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squares support vector machine (MLS-SVM) is a special least square SVM (LS-SVM), which extends the application of the SVM to the imageprocessing. Based on the MLS-SVM, a family of filters for the approximation of partial derivatives of the digital image surface is designed. Prior information (e.g., local dominant orientation) are incorporated in a two dimension weighted function. The weighted MLS-SVM with the radial basis function kernel is applied to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.
This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specified...
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This paper investigated the performances of a well-known car-following model with numerical simulations in describing the deceleration process induced by the motion of a leading car. A leading car with a pre-specified speed profile was used to test the above model. The results show that this model is to some extent deficient in performing the process aforementioned. Modifications of the model to overcome these deficiencies were demonstrated and a modified car-following model was proposed accordingly. Furthermore, the delay time of car motion of the new model were studied.
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial e...
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ISBN:
(纸本)0769523722
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.
In this paper, we introduce methods to extract low-level features for soccer video analysis. A new method is proposed to segment players by using a mean distributed color feature. In order to discriminate which team t...
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ISBN:
(纸本)0780390059
In this paper, we introduce methods to extract low-level features for soccer video analysis. A new method is proposed to segment players by using a mean distributed color feature. In order to discriminate which team the player belongs to, we use mutual chromatic correlation degree of players to identify team without extracting templates of players in advance. Experimental results are included to show the effectiveness of the method.
Many vision-related processing tasks, including edge detection and image segmentation, can be performed more easily when all objects in the scene are in good focus. However, in practice, this may not be always feasibl...
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The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-ba...
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The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their reccgnition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis (PCA) with different transformation inputs,can achieve satisfactory accuracy even at low SNR (as low as 2 dB). Due to the properties of spectrogram, the statistical patternrecognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.
A novel algorithm of global motion estimation is proposed. First, through Gabor wavelet transform (GWT), a kind of energy distribution of image is obtained and checkpoints are selected according to a probability decis...
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A novel algorithm of global motion estimation is proposed. First, through Gabor wavelet transform (GWT), a kind of energy distribution of image is obtained and checkpoints are selected according to a probability decision approach proposed. Then, the initialized motion vectors are obtained via a hierarchal block-matching based on these ***, by employing a 3-parameter motion model, precise parameters of global motion are found. From the experiment, the algorithm is reliable and robust.
An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified ...
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An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified expectation-maximization (EM) algorithm. The technique of pre-assigning a class number was employed to decrease the computation burden. Therefore the task of image segmentation was implemented by the network. The experiment results show that it is feasible to apply the improved MBNN to image segmentation since a priori knowledge is excellently combined with local statistical correlation.
Vessel segmentation is the base of 3d reconstruction of Digital Subtraction Angiograph (DSA) images. This paper proposes a framework of adaptive local thresholding based on a verification-based approach for vessel seg...
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Vessel segmentation is the base of 3d reconstruction of Digital Subtraction Angiograph (DSA) images. This paper proposes a framework of adaptive local thresholding based on a verification-based approach for vessel segmentation of DSA images. The original DSA image is firstly divided into overlapping subimages according to a priori knowledge of the diameter of vessels. We implement a hypothesis test to determine whether each subimage contains vessels and then choose an optimal threshold respectively for every subimage previously determined to contain vessels, with a secondary verification process to exclude the condition that the subregion only containing the background but misclassified as one containing vessels by the hypothesis test. Finally an overall binarization of the original image is achieved by combining the thresholded subimages. Experiments demonstrate superior performance over global thresholding and some adaptive local thresholding methods.
In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. Blood vessel segmentation is a main problem for 3D vascular reconstruction....
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In clinical practice, digital subtraction angiography (DSA) is a powerful technique for the visualization of blood vessels in the human body. Blood vessel segmentation is a main problem for 3D vascular reconstruction. In this paper, we propose a new adaptive thresholding method for the segmentation of DSA images. Each pixel of the DSA images is declared to be a vessel/background point with regard to a threshold and a few local characteristic limits depending on some information contained in the pixel neighborhood window. The size of the neighborhood window is set according to a priori knowledge of the diameter of vessels to make sure that each window contains the background definitely. Some experiments on cerebral DSA images are given, which show that our proposed method yields better results than global thresholding methods and some other local thresholding methods do.
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