As an essential step in brain studies, measuring the distribution of major brain tissues, including gray matter, white matter and cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensi...
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As an essential step in brain studies, measuring the distribution of major brain tissues, including gray matter, white matter and cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive research efforts over the past years. Many brain tissue differentiation methods resulted from these efforts are based on the finite statistical mixture model, which however, in spite of its computational efficiency, is not strictly followed due to the intrinsically limited quality of MRI data and may lead to less accurate results. In this paper, a novel large-scale variational Bayesian inference (LS-VBI) learning algorithm is proposed for automated brain MRI voxels classification. To cope with the complexity and dynamic nature of MRI data, this algorithm uses a large number of local statistical models, in each of which all statistical parameters are assumed to be random variables sampled from conjugate prior distributions. Those models are learned using variational Bayesian inference and combined to predict the class label of each brain voxel. This algorithm has been evaluated against several state-of-the-art brain tissue segmentation methods on both synthetic and clinical brain MRI data sets. Our results show that the proposed algorithm can classify brain voxels more effectively and provide more precise distribution of major brain tissues.
The discrete-time double-integrator consensus and rendezvous problems are both addressed for distributed multiagent systems with directed switching topologies and input *** develop model predictive control algorithms ...
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ISBN:
(纸本)9781479947249
The discrete-time double-integrator consensus and rendezvous problems are both addressed for distributed multiagent systems with directed switching topologies and input *** develop model predictive control algorithms to achieve stable consensus or rendezvous provided that the proximity nets always have a directed spanning tree and the sampling period is sufficiently ***,the control horizon is extended to larger than one as well,which endows sufficient degrees of freedom to facilitate controller *** simulations are finally conducted to show the effectiveness of the control algorithms.
This paper proposes an improved online framework based on Compressive Tracker (CT) for multiple pedestrian tracking in surveillance videos. The CT method proposed by Zhang et al was originally used for single object t...
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ISBN:
(纸本)9781479939046
This paper proposes an improved online framework based on Compressive Tracker (CT) for multiple pedestrian tracking in surveillance videos. The CT method proposed by Zhang et al was originally used for single object tracking, and fails to make use of context information during the tracking process. To overcome the crucial drawbacks of CT, our method implements multi-scale tracking and fuse CT with Kalman Filter to take advantage of the spatio-temporal context information. Additionally, incorporated with the detection of foreground blobs and an online learned detector, this paper introduces a supplementary mechanism to handle the inter-target occlusion. Experimental results on realistic sequences demonstrate the effectiveness of our approach.
In this paper, we introduce the development of object tracking. In particular, we introduce several kinds of target tracking algorithm based on sparse coding, including a robust visual tracking method by casting track...
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ISBN:
(纸本)9781479970063
In this paper, we introduce the development of object tracking. In particular, we introduce several kinds of target tracking algorithm based on sparse coding, including a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework, kernel sparse tracking with compressive sensing, and real-time compressive tracking. Show the concept of sparse representation and compressed sensing, analyze the meaning of the sparse representation in the target tracking, and compare the algorithm.
In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert's semantic knowledge (a priori knowledge) and th...
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A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this *** this system,the input frames are divided into key fra...
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A novel Compressed-Sensing-based(CS-based)Distributed Video Coding(DVC)system,called Distributed Adaptive Compressed Video Sensing(DISACOS),is proposed in this *** this system,the input frames are divided into key frames and non-key frames,which are encoded by block CS *** key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber(SPL)algorithm using multi-hypothesis *** the non-key frames,a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information(SI)generated by the conventional interpolation or extrapolation at the decoder;then,another group of CS measurements are sampled again upon the decoder’s *** fully utilise the CS measurements,we adaptively allocate these measurements to each block in terms of different edge ***,the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the *** results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.
In this paper,we propose a novel approach to recognise human activities from a different *** appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,there must be...
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In this paper,we propose a novel approach to recognise human activities from a different *** appearance-based recognition methods have been shown to be unsuitable for action recognition for varying views,there must be some regularity among the same action sequences of different *** matrices appear to be relative stable across ***,the ability to effectively realise this stability is a *** this paper,we extract the shape-flow descriptor as the low-level feature and then choose the same number of key frames from the action ***-similarity matrices are obtained by computing the similarity between any pair of the key *** diagonal features of the similarity matrices are extracted as the highlevel feature representation of the action sequence and Support Vector Machines(SVM) is employed for *** test our approach on the IXMAS multi-view data *** proposed approach is simple but effective when compared with other algorithms.
To validate the robust stability of the flight control system of hypersonic flight vehicle, which suffers from a large number of parametrical uncertainties, a new clearance framework based on structural singular value...
A new object tracking scheme for multi-camera surveillance with non-overlapping views is proposed in this paper. Brightness transfer function (BTF) is used to establish relative appearance correspondence between diffe...
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High Efficiency Video Coding (HEVC) achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computat...
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ISBN:
(纸本)9781479947164
High Efficiency Video Coding (HEVC) achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, we propose a fast CU depth decision scheme to reduce the encoder complexity for HEVC. Firstly, the relationship between rate-distortion (R-D) cost and CU depth is explored carefully with Mean Squared Error (MSE) and Number of Encoded Bits (NEB) metrics. Then CU splitting is modeled as a binary classification problem and resolved by an offline trained Support Vector Machine (SVM) model. The experimental results show that the proposed algorithm achieves up to 59% running-time reduction with negligible loss in terms of PSNR and bit rate.
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