As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step tow...
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This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to reco...
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This paper proposed a novel model-based feature representation method to characterize human walking properties for individual recognition by gait. First, a new spatial point reconstruction approach is proposed to recover the coordinates of 3D points from 2D images by the related coordinate conversion factor (CCF). The images are captured by a monocular camera. Second, the human body is represented by a connected three-stick model. Then the parameters of the body model are recovered by the method of projective geometry using the related CCF. Finally, the gait feature composed of those parameters is defined, and it is proved by experiments that those features can partially avoid the influence of viewing angles between the optical axis of the camera and walking direction of the subject.
This paper presents a fast connected component labeling algorithm based on line description method and optimized tree Union-Find strategy. The algorithm transforms the pixel-connected issue, which most of proposed alg...
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This paper presents a fast connected component labeling algorithm based on line description method and optimized tree Union-Find strategy. The algorithm transforms the pixel-connected issue, which most of proposed algorithms focus on, into line-connected issue. This algorithm is comprised of three phrases, line extraction, connected component identification and label assignment. The line description method transforms the connected pixels into line form for reducing the scan time. While the new tree Union-Find strategy diminishes the redundant root compare operations. A comparison analysis is performed with other optimized famous component labeling algorithms. Our algorithm has shown an outstanding performance with respect to the processing time, which achieves 1.1~8 times as fast as the other algorithms in various test cases.
To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel...
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To address two challenging problems in infrared target tracking, target appearance changes and unpre- dictable abrupt motions, a novel particle filtering based tracking algorithm is introduced. In this method, a novel saliency model is proposed to distinguish the salient target from background, and the eigenspace model is invoked to adapt target appearance changes. To account for the abrupt motions efficiently, a two- step sampling method is proposed to combine the two observation models. The proposed tracking method is demonstrated through two real infrared image sequences, which include the changes of luminance and size, and the drastic abrupt motions of the target.
In the medical diagnostic computed tomography (CT) systems, the x-ray tube usually emits photons with a polychromatic spectrum, resulting in beam hardening artifacts in the reconstructed images. Although the bone corr...
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In the medical diagnostic computed tomography (CT) systems, the x-ray tube usually emits photons with a polychromatic spectrum, resulting in beam hardening artifacts in the reconstructed images. Although the bone correction method is extensively used to compensate for the beam hardening artifacts, its performance crucially depends on the empirical choice of a scaling factor. To overcome this shortcoming, here we propose two adaptive correction methods, which utilize the Helgasson-Ludwig (H-L) consistency condition to determine the optimal scaling factor and the corresponding coefficient vector. Our numerical simulation results demonstrate the effectiveness of the proposed methods.
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.
Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This...
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ISBN:
(纸本)9781424435036
Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.
For ultrasound heart images artery segmentation, this paper introduces a novel method based on speckle denoising with nonlinear coherent diffusion. This method reduces the segmentation sensitivity to image noise and s...
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ISBN:
(纸本)9781601321190
For ultrasound heart images artery segmentation, this paper introduces a novel method based on speckle denoising with nonlinear coherent diffusion. This method reduces the segmentation sensitivity to image noise and speeds up level-set evolution largely. We do denoising process in narrow band region with an improved nonlinear coherent diffusion (INCD) to improve ultrasound images local region coherence property and preserve the edge to speed up evolution of level set. The results of segmentation show: After same iterations, zero level set fronts move faster in filtered images with INCD than filtered with other methods, the proposed method is more accurate and efficient.
In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is *** using Gaussian pyramid to process two multitemporal images respectively,two image pyra...
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In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is *** using Gaussian pyramid to process two multitemporal images respectively,two image pyramids are *** difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image *** resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them,a map being similar to the difference image is *** difference image is generated by point-by-point subtraction between the two multitemporal images *** last,the Bayes rule is used to distinguish the changed *** synthetic and real data sets are used to evaluate the performance of the proposed *** results show that the map from the proposed technique is more robust to noise than the difference image.
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