Blurred images are caused by many factors such as defocus, motion, and atmospheric turbulence. Due to the unknown various factors that cannot be distinguished in the blurred image, it is necessary to propose a unified...
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The restoration of rotational motion blurred image involves a lot of interpolations operators in rectangular-to-polar transformation and its inversion of polar-to-rectangular. The technique of interpolation determines...
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In this paper, we propose a novel algorithm for gait recognition. Binarized silhouette of a motion object is first segmented from color image, and then, spatio-temporal (XYT) volume is constructed by using these binar...
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In this paper, we propose a novel algorithm for gait recognition. Binarized silhouette of a motion object is first segmented from color image, and then, spatio-temporal (XYT) volume is constructed by using these binarized silhouettes, and cut at knee and hip height. Next, energy images are extracted by projecting these three individual XYT volumes onto X-T plane, respectively. Fourier Transform is employed as a processing step to achieve translation invariant for the silhouette sequences which are captured from the subjects walk in different speed. Then three frequency-domain feature vectors are fused. AdaBoost is used to select a small set of critical features from all of the features. Nearest Neighbor and Support Vector Machine (SVM) classifier are finally executed to produce final decision, respectively. The experiments are carried on one of the largest public gait database: the CASIA database. The experimental results show that the proposed algorithm is efficient for human gait recognition, and achieves competitive performance.
In an automatic face recognition system, it still remains a challenge to improve the robustness to aging. In this paper, we present a novel approach to address age invariant face recognition, by formulating it as a gr...
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In an automatic face recognition system, it still remains a challenge to improve the robustness to aging. In this paper, we present a novel approach to address age invariant face recognition, by formulating it as a graph matching problem. In contrast to the majority of tasks in the literature that only make use of robust texture features, this method generates a graph from a set of fiducial landmarks of each face, which captures the texture clues that tend to be stable in a period as well as the common facial geometry configuration. The nodes of the graph denote the texture of a face area around a landmark, and the edges correspond to the geometry topology of the face. For each area, the age invariant texture information is extracted by a discriminative and compact feature encoded in the Local Gabor Binary Pattern Histogram Sequence (LGBPHS) projected in an LDA subspace. An objective function is then designed to match graphs for the purpose of registration and identification. Experiments are carried out on the FG-NET Aging database, and the results achieved outperform the state of the art ones, which clearly demonstrate the effectiveness and robustness of the proposed method in face recognition across age variations.
A new method to detect laser parameter is proposed in this paper. The improved Michelson Interferometer is used as experiment system to detect parameter. In the proposed detection method, the interference images in va...
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Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmen...
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Recently, asymmetric 3D-2D face recognition has been paid increasing attention. It enrolls in textured 3D faces and performs identification using only 2D facial images, therefore it generally achieves a better result ...
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In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for im...
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ISBN:
(纸本)9780819469519
In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for image fusion. Each image from different sensors could be decomposed into a low frequency image and a series of high frequency images of different directions by multi-sacle NSCT. For low and high frequency images, they are fused based on local-contrast enhancement and definition respectively. Finally, fused image is reconstructed from low and high frequency fused images. Experiment demonstrates that NSCT could preserve edge significantly and the fusion rule based on region segmentation performances well in local-contrast enhancement.
A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not ...
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A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.
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.
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