The sliding window method will cause the severe unbalanced dataset problem. In this paper, under-sample the majority class method is adopted to solve this problem, and SVM is used to classify the processed data. The b...
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Based on the thought of ensemble forecast, Ensemble Kalman filter (EnKF) gives a typical implementation of Bayesian estimation in Monte-Carlo simulation. However, the sampling process of particles excessively relies o...
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Based on the thought of ensemble forecast, Ensemble Kalman filter (EnKF) gives a typical implementation of Bayesian estimation in Monte-Carlo simulation. However, the sampling process of particles excessively relies on the priori modeling information form the system state transition in EnKF, which inevitably causes the particle degeneracy phenomenon. In this paper, we propose a novel Ensemble Kalman filtering algorithm based on weights optimization of sampling particles. Firstly, combining with the importance-sampling technique, the contribution degree of state estimation from particles is effectively measured. Secondly, by increasing particles numbers with high weights and decreasing particles numbers with low weights, the sampling particles set is optimized in the global sense. In addition, the estimated method of importance weights on the basis of virtual observation is constructed in the framework of EnKF, and the adverse effects on the reliability and stability of particle weights caused by the observation random noise are improved. The experimental results show the feasibility and efficiency of the proposed algorithm.
Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasing...
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Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.
The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A waters...
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The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradient of the classified image, dividing the image into markers, checking the Marker image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker image. Finally region merging is done according to region average intensity and two edge strengths (T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation result is one closed boundary per actual region in the image.
Research on object tracking has been an active field because of its fundamental roles in surveillance and monitoring. In this paper, a new adaptive algorithm for fast target tracking based on hierarchical block matchi...
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A support vector regression(SVR) based color image restoration algorithm is *** test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degrade...
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A support vector regression(SVR) based color image restoration algorithm is *** test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original *** comparisons of the proposed algorithm versus traditional filtering algorithms are *** results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterative blind deconvolution algorithm.
In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segme...
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This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff pr...
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This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient （HTffOG） feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.
Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems,...
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Linear discrimiant analysis (LDA) has been used in face recognition. But it is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination. In order to overcome these problems, kernel discriminant analysis for face recognition is presented. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are given.
Segmentation of the bladder in computerized tomography(CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder cont...
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Segmentation of the bladder in computerized tomography(CT) images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First,we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder,which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity,specificity,positive predictive value,negative predictive value,and Hausdorff distance are 86.5%,96.3%,90.5%,96.5%,and 2.8 pixels,respectively. The results show that the bladder can be accurately segmented.
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