Action recognition is an important topic in computer vision and most current work focuses on view-dependent representations. In this paper, we develop a novel free viewpoint action recognition based on Self-similarity...
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ISBN:
(纸本)9781467321969
Action recognition is an important topic in computer vision and most current work focuses on view-dependent representations. In this paper, we develop a novel free viewpoint action recognition based on Self-similarity matrix (SSM), which tends to be stable across views. We choose Local Self-similarity (LSS) descriptor as our low-level feature, then SSM is calculated by computing the similarity between any pair of frame features. Each video sequence is represented using a diagonal descriptor vector extracted from the SSM. Support Vector Machines (SVM) is employed for classification. The encouraging experimental results on the public IXMAS multi-view data set demonstrate effectiveness of the proposed method.
This paper proposes a global stereo correspondence using robust matching likelihoods and minimum spanning tree （MST） leveraged smooth priors in a probabilistic graphical model framework. The matching likelihoods of ...
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ISBN:
(纸本)9781467321969
This paper proposes a global stereo correspondence using robust matching likelihoods and minimum spanning tree （MST） leveraged smooth priors in a probabilistic graphical model framework. The matching likelihoods of the stereo correspondence can be robustly constructed as data term by aggregating initial matching costs from Weber local descriptors using an unsymmetrical guided filtering in a linear model. The disparity priors are devised as smooth term to characterize the smoothness constraints leveraged by the MST structure. The presented stereo approach provides an effective and efficient way to reflect robust visual dissimilarity and resolve local and regional discontinuities. Experiments demonstrate that the proposed global stereo matching method can produce piecewise smooth, accurate and dense disparity map, while removing effectively the visual ambiguity of the stereo matching problem.
This paper proposes a novel global stereo matching method using aggregated likelihoods and multi-scale priors. The likelihoods of dense stereo correspondences as data term can be robustly expressed by aggregated match...
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A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of...
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ISBN:
(纸本)9781467321969
A new scheme of object tracking with mean shift is put forward in this paper. At first, texture feature is fused in the processing by Local Ternary Pattern (LTP). Since LTP is sensitive to local noise, least median of squares (LMedS) algorithm is used to adaptively calculate the noise threshold for accurate estimation of the LTP texture information. Furthermore, target scale and orientation is estimated in case of partial occlusion or rotation, so as to realize robust object tracking. Experimental results show that the proposed algorithm can acquire robust tracking performance under complex background .
Distributed video coding (DVC) is a novel video coding paradigm. One approach to DVC is Wyner-Ziv distributed video coding. The accuracy of the correlation noise model can influence the performance of the video coder ...
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Distributed video coding (DVC) is a novel video coding paradigm. One approach to DVC is Wyner-Ziv distributed video coding. The accuracy of the correlation noise model can influence the performance of the video coder directly. In order to enhance the accuracy of the distribution model, EM algorithm based mixture Laplace-uniform distribution model and basic Laplace-uniform distribution model for DCT alternating current coefficients are established. Then the model is selected adaptively using fuzzy inference. Experimental results suggest that the proposed mixture correlation noise model can describe the heavy tail and sudden change of the noise accurately at high rate and make significant improvement on the coding efficiency compared with the DISCOVER's noise model. Meanwhile, fuzzy inference based adaptive noise model selection method can reduce the operation complexity to some extent, while not influencing rate-distortion performance.
Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost...
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Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder. At the decoder, compressed video can be efficiently reconstructed. Our algorithm can be useful in those video applications that require very low complex encoders. Simulation results show that our scheme compares favorably with existing schemes at a much lower implementation cost.
Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on comp...
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Compressive sensing (CS) is a new technique for data sampling and compression simultaneously. In this paper, we propose a novel distributed video coding algorithm with dynamic measurement rate allocation based on compressive sensing principles, where almost all computation burdens can be shifted to the decoder, resulting in a very low-complexity encoder. So the proposed algorithm can be useful in those video applications that require very low complex encoders. At the decoder, the compressed video can be efficiently reconstructed with adaptive dictionary learning. The simulation results show that the proposed algorithm outperforms the distributed compressive video sensing with non-adaptive learning local dictionary and global dictionary.
Efficient video transmission over unreliable channels may encounter huge challenge due to unavoidable bit error or packets loss. Error concealment (EC) techniques at the decoder side have been developed to recover the...
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This paper proposes an improved image interpolation method based on the soft-decision adaptive interpolation (SAI) algorithm. Natural images often contain repeatable patterns and structures throughout the image, which...
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Camera calibration is the essential step of obtaining 3D information from 2D views in the field of computer vision, which is widely used in the area of 3D reconstruction, navigation, visual supervision, etc. A camera ...
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