This paper presents a novel facial localization method for 3D face in the presence of facial pose and expression variation. An idea of using Multi-level Partition of Unity (MPU) Implicits in a hierarchical way is prop...
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Tracking-by-detection is a popular tracking framework nowadays. The paradigm determines that detections will bring huge impact on the final tracking result. Based on the idea, to improve the tracking precision, we pro...
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The Based on the background of anti counterfeit of commercial bills, a novel digital watermarking method is proposed in this paper. The watermarking algorithm is based on a class of orthogonal function systems - V sys...
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In the presence of non-gaussian noise, we propose a method for the detection of underwater ship-radiated signal. The wavelet decomposition of the underwater signal yields a natural tree structure, which is further mod...
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A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the color labels of image so as to take advantage of the traditional approaches developed for gray-sc...
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The performance of the kernel-based learning algorithms, such as SVM, depends heavily on the proper choice of the kernel parameter. It is desirable for the kernel machines to work on the optimal kernel parameter that ...
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Aiming at the adverse influence of correlation between measurement noise and process noise for filtering precision in nonlinear system estimation, Firstly, Rao-Blackwellised modeling technique is introduced to disasse...
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For medical image classification, annotations for images are laborious and expensive, which is suitable for the application of semi-supervised learning. Mainstream semi-supervised learning methods develop a consistenc...
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Hyper-graph matching algorithm describes the whole structure of object by high-order topology. Previous work has presented many methods to build and solve the problem model. This paper mainly focuses on feature descri...
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This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is clos...
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This paper introduces a new approach, nearest convex hull (NCH), for remote sensing classification. NCH is an intuitive classification method which labels the test point as the training class whose convex hull is closest to it. Some attractive advantages of this learning algorithm are the robustness to noises and the scale of training samples, the straightforward way to handle multi-class tasks, and most of all the capability of processing high dimensional and nonlinear data. In our work, we deduce the NCH algorithm again basing on theories of the computational geometry, from which a simpler implementation of it is presented. Then we apply it to real-world remote problems and compare it with two other state-of-arts classifiers: K-NN and SVM. Experiments in this paper confirm the promising performance of NCH for remote sensing classification.
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