The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual d...
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The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to the tensor case by the definition of tensor trace norm in. Furthermore, the problem of tensor is formulated as a convex optimization, which is much harder than its matrix form. Thus, we develop a high quality algorithm to efficiently solve the problem. Our experiments show potential applications of our method and indicate a robust and reliable solution.
In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remote sensing images is presented. Knowledge base is a critical com...
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In order to protect the copyright of the image, in this paper proposed a novel important sub-tree (Istree) digital watermarking algorithm based on contourlet transform. First, Shuffling is applied by watermarking imag...
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In order to protect the copyright of the image, in this paper proposed a novel important sub-tree (Istree) digital watermarking algorithm based on contourlet transform. First, Shuffling is applied by watermarking image for increasing robustness. Second, the original image are decomposed three levels by contourlet transform, and then analysis sub-bands in all directions, according to various levels sub-bands in all direction structure like tree. And find important sub-tree, then Scrambling after the watermark image sequence is embedded in important sub-tree. The experimental results show that the algorithm has a certain degree of robustness and can resistance attack.
Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorith...
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Motion blur detection and the relevant blurring parameter estimation are important for many computer vision tasks. The contribution of this paper is in two folds. First, we propose a closed-form solution for motion di...
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Motion blur detection and the relevant blurring parameter estimation are important for many computer vision tasks. The contribution of this paper is in two folds. First, we propose a closed-form solution for motion direction estimation on blurred image. Secondly, a novel method is proposed for motion blurred region detection. The proposed direction estimation is based on measurement of lowest directional high-frequency energy. Compared with traditional methods, it will improve accuracy with less computational cost. Moreover, the proposed motion blurred region detection can efficiently estimate blurred regions without Point Spread Function estimation. Encouraging results are shown by experiments.
The Multiple Signal Classification (MUSIC) method is a typical method for high-resolution Direction Of Arrival(DOA) and frequency estimation. Usually it performs spectrum search in certain grid space, which inevitably...
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ISBN:
(纸本)9781424472352
The Multiple Signal Classification (MUSIC) method is a typical method for high-resolution Direction Of Arrival(DOA) and frequency estimation. Usually it performs spectrum search in certain grid space, which inevitably leads to high computational cost in the muti-dimensional case, for example the search for frequency and azimuth at the same time. To overcome this problem, in this paper, we introduced Ant Colony Optimization(ACO) to work with MUSIC. A new kind of ACO for continuous domain featured by Gauss kernel function is used to sample the MUSIC spectrum, which is regarded as the fitness function in the process. The resulted estimator is called Ant Colony Optimization based MUSIC (ACO-MUSIC). Simulations show that ACO-MUSIC not only reduces the computational complexity greatly but also maintains the excellent performance of the original MUSIC estimator.
In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a si...
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ISBN:
(纸本)9781424475421
In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids and 2-D SIFT), and ii) the higher-order statistical models of interest points, which aims to capture the global information of the actor. We construct video representation in terms of local space-time features and global features and integrate such representations with hyper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed approach is effective. An additional experiment shows that using both local and global features provides a richer representation of human action when compared to the use of a single feature type.
Based on the discrete Fourier transformation (DFT) and Hough transforms, a novel digital watermarking method is proposed. The experiment results show that the algorithm is more robust than the traditional watermark al...
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Based on the discrete Fourier transformation (DFT) and Hough transforms, a novel digital watermarking method is proposed. The experiment results show that the algorithm is more robust than the traditional watermark algorithm. The proposed algorithm can endure severe attacks such as printing-scanning, very high loss in its data or data packets, scaling and rotating. The most advantage of the algorithm presented in this paper is that it is robust for the first time of print and scan, but fragile for the second time of print and scan. So this method can be used in the anti counterfeit of certificates.
This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic...
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This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible dif...
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ISBN:
(纸本)9781424475421
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.
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