The design of DNA sequence plays an important role in improving the reliability of DNA computation. Proper constrained terms that DNA sequence should satisfy are selected, and then the evaluation formulas of each DNA ...
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The design of DNA sequence plays an important role in improving the reliability of DNA computation. Proper constrained terms that DNA sequence should satisfy are selected, and then the evaluation formulas of each DNA individual corresponding to the selected constrained terms are proposed. The heuristic improved genetic algorithm (GA)/simulated annealing (SA) algorithm is presented to solve the multi-objective optimize problem, and the DNA sequence design system is developed. Furthermore, an example is illustrated to show the efficiency of our method given here.
The technologies of intra prediction and MBAFF were introduced, and a new intra prediction mode based on the characteristics of spatial distribution in interlaced video was proposed. The spatial correlation of five lu...
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The technologies of intra prediction and MBAFF were introduced, and a new intra prediction mode based on the characteristics of spatial distribution in interlaced video was proposed. The spatial correlation of five luma intra prediction modes in AVS-P2 and the new mode were analyzed. From the analysis result, it can be concluded that the new mode can exploit the spatial correlation better and predict the samples more precisely than the existed ones. The experimental results showed that the average gain in peak signal to noise ratio was above 0.12dB and the average reduction in bit-rate was above 1.77%, so the proposed mode is an effective prediction mode for improvement of coding performance.
An innovative edge detection algorithm using the support value transform is presented in this *** on the support value transform, the multi scale support value images are extracted from the *** resolution of an image ...
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An innovative edge detection algorithm using the support value transform is presented in this *** on the support value transform, the multi scale support value images are extracted from the *** resolution of an image is directly related to the proper scale for edge detection, and the second level support value image is used to locate the edge positions by their zero *** experiments are carried out and sharp image edges are obtained from a variety of sample *** with many other existing methods, including LoG and Canny detectors, the proposed algorithm is superior to the LOG and Roberts approach.
A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multi...
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A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge intbrmation. This method is adaptive to local image details, and can achieve bet, ter performance than the methods of state of the art.
Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for...
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Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.
A platform of the Internet-based teleoperation system with an omni-directional mobile robot which has a five DOFs robot arm is constructed. Remote control of the robot through the Internet is implemented. The system i...
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A platform of the Internet-based teleoperation system with an omni-directional mobile robot which has a five DOFs robot arm is constructed. Remote control of the robot through the Internet is implemented. The system is featured as low-cost and user interface friendly: remote users can control the robot through the Internet just by a client program in a general computer. The client computer can receive the live video and environment information measured by sensors. With the help of the remote video and the local simulation, users can easily communicate with the robot. Different modules are proposed and the implementation method of the system is presented. Related experiments are conducted to test the validity of the proposed system.
Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative mod...
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Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.
We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability...
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ISBN:
(纸本)9781424406715;1424406714
We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.
An ordination-fuzzy min-max neural network (OFMM) based on non-metric multidimensional scaling (MDS) is proposed to solve the classification problems of unlabelled input pattern. Firstly, all the input patterns are so...
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An ordination-fuzzy min-max neural network (OFMM) based on non-metric multidimensional scaling (MDS) is proposed to solve the classification problems of unlabelled input pattern. Firstly, all the input patterns are sorted by MDS to get their similarity measures. Then these measures are used to supervise the following expansion and contraction stage of hyperboxes for classification. OFMM shows the improvements in the validity of unlabelled patterns classification, the network structure, and training time. The experimental results on standard dataset demonstrate that OFMM is a practical and effective classifier which is superior to the traditional general-fuzzy min -max neural network (GFMM).
Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize high...
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ISBN:
(纸本)9781424406715;1424406714
Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.
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