The lateral prefrontal cortex (lPFC) plays crucial roles in executive functions, including working memory and behavioral planning. The functions of lPFC require conservation of its limited neuronal resources. Herein, ...
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ISBN:
(纸本)9783031301070;9783031301087
The lateral prefrontal cortex (lPFC) plays crucial roles in executive functions, including working memory and behavioral planning. The functions of lPFC require conservation of its limited neuronal resources. Herein, we examined lPFC neuronal activities in monkeys during a path-planning task that required behavioral planning and working memory. We analyzed the coding dynamics of final-goal neurons, and found selective and sustained activities toward the final goal, reflecting working memory. Putative excitatory pyramidal neurons shifted their scheme from discrete to collective coding during the preparatory period of the task, whereas inhibitory interneurons used a collective coding scheme.
We propose a novel adaptive locality-constrained regularized robust coding (ALRRC) for image recognition by considering both the effects of the test sample's features and the local relationships between the test s...
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We propose a novel adaptive locality-constrained regularized robust coding (ALRRC) for image recognition by considering both the effects of the test sample's features and the local relationships between the test sample and the training samples in the linear coding procedure. By adaptively calculating out a feature weight matrix of a test sample, ALRRC measures the effects of the test sample's features in the linear coding procedure. Furthermore, ALRRC can obtain the weighted test sample by multiplying the test sample by the feature weight matrix, which has reduced the roles of the aberrant features as far as possible. Similarly, ALRRC can get the weighted training samples by multiplying the training samples by the feature weight matrix. Moreover, using the similarities between the weighted test sample and weighted training samples, ALRRC adaptively calculates out a locality-constrained matrix that can truly characterize the local relationships between the test sample and the training samples. Finally, by incorporating the feature weight matrix and locality-constrained matrix into the linear coding framework, ALRRC is inclined to select the true local training samples to represent the test sample. In addition, we also propose an iterative algorithm to solve the optimization problem of ALRRC. Experimental results on several image databases show that ALRRC is more effective and robust than state-of-the-art linear coding-based methods.
The increased compression ratios achieved by the High Efficiency Video coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality d...
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The increased compression ratios achieved by the High Efficiency Video coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality degradation. This paper proposes a method based on a two-stage approach to improve the error robustness of HEVC streaming, by reducing temporal error propagation in the case of frame loss. The prediction mismatch that occurs at the decoder after frame loss is reduced through the following two stages. First, at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimization, which distributes the use of reference pictures, by reducing the number of prediction units that depend on a single reference. Second, at the streaming stage, a motion vector (MV) prioritization algorithm, based on spatial dependencies, selects an optimal subset of MVs to be transmitted, redundantly, as side information to reduce mismatched MV predictions at the decoder. The simulation results show that the proposed method significantly reduces the effect of temporal error propagation. Compared with the reference HEVC, the proposed reference picture selection method is able to improve the video quality at low-packet-loss rates (e.g., 1%) using the same bitrate, achieving quality gains up to 2.3 dB for 10% of packet loss ratio. It is shown, for instance, that the redundant MVs are able to boost the performance achieving quality gains of 3 dB when compared with the reference HEVC, at the cost using 4% increase in total bitrate.
The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, regularized robust codin...
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The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which a weight value is assigned to each pixel to measure its importance in the coding procedure and the spatial consistence of occluded pixels is exploited by the pixel weight learning (PWL) model. Efficient algorithms were also proposed to fast learn each pixel's weight value. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency. (C) 2016 Elsevier B.V. All rights reserved.
The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized rob...
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ISBN:
(数字)9783662485705
ISBN:
(纸本)9783662485705;9783662485699
The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which the spatial consistence of occluded pixels was exploited by pixel weight learning (PWL) model. Efficient algorithms were also proposed to fastly learn the pixel's weight and accurately recover the occluded area. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency.
Face hallucination (FH) is to produce face images with High Resolution (HR) from Low Resolution (LR) observations. Unfortunately, most existing FH methods fail to make full use of the local geometrical information, es...
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Face hallucination (FH) is to produce face images with High Resolution (HR) from Low Resolution (LR) observations. Unfortunately, most existing FH methods fail to make full use of the local geometrical information, especially when the LR images are corrupted by noise. Inspired by the observation that regions with large scales can provide much useful information, in this paper we propose a robust Locality-constrained Multiscale coding (RLMC) based method to forecast HR face images while suppressing noise and outliers. In RLMC, a weight vector is used in the loss function to ease the effect of outliers in data representation. Furthermore, inspired by the observation that abundant local information can be exploited by jointly representing overlapping patches with multiple scales. Simultaneously encoding multiple scale patches encourages different scales to share complementary information, which admits the proposed method to generate more appropriate coefficients for super-resolution reconstruction. Experimental results verified the effectiveness of the proposed method in terms of both quantitative measurements and visual impressions. (C) 2019 Published by Elsevier Inc.
Low-delay hierarchical coding structure (LD-HCS), as one of the most crucial components in the High Efficiency Video coding (HEVC) standard, substantially complicates the temporal reference relationship and achieves b...
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Low-delay hierarchical coding structure (LD-HCS), as one of the most crucial components in the High Efficiency Video coding (HEVC) standard, substantially complicates the temporal reference relationship and achieves better compression ratios. There have been lots of research work on increasing the error resilience of HEVC coding. However, most of them neglect the characteristics of LD-HCS and cannot achieve the optimum trade-off between coding efficiency and error robustness. To this end, in this paper, a non-feedback reference picture selection algorithm is proposed to improve the error robustness of HEVC streaming under LD-HCS, by reducing temporal error propagation in case of packet loss. Specifically, the temporal relationship among different frames under LD-HCS is firstly analyzed, and then frames with the lowest temporal layer are considered as the potential key-frame which is capable of preventing error propagation. Secondly, the end-to-end distortions of using two different reference schemes for the potential key-frame are compared based on an overall distortion estimation model, and the reference scheme that has less total distortion is chosen ultimately. Moreover, the potential key-frame will be encoded entirely in intra-mode if the error propagation is severe. Extensive experiment results demonstrate that the proposed reference picture selection method significantly reduces the effect of temporal error propagation. Compared to the state-of-the-art approaches, the proposed method is able to improve the video quality over error-prone networks.
Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the repr...
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Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l(2)-norm or l(1)-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (I (RC)-C-3) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.
Recently, locality-constrained linear coding (LLC) has been drawn great attentions and been widely used in image processing and computer vision tasks. However, the conventional LLC model is always fragile to outliers....
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Recently, locality-constrained linear coding (LLC) has been drawn great attentions and been widely used in image processing and computer vision tasks. However, the conventional LLC model is always fragile to outliers. In this paper, we present a robust locality-constrained bi-layer representation model to simultaneously hallucinate the face images and suppress noise and outliers with the assistant of a group of training samples. The proposed scheme is not only able to capture the nonlinear manifold structure but also robust to outliers by incorporating a weight vector into the objective function to subtly tune the contribution of each pixel offered in the objective. Furthermore, a high-resolution (HR) layer is employed to compensate the missed information in the low-resolution (LR) space for coding. The use of two layers (the LR layer and the HR layer) is expected to expose the complicated correlation between the LR and HR patch spaces, which helps to obtain the desirable coefficients to reconstruct the final HR face. The experimental results demonstrate that the proposed method outperforms the state-of-the-art image super-resolution methods in terms of both quantitative measurements and visual effects.
The work promotes reliable dynamical validations for transmitted e-documents using session wise randomly hidden secret multi-signatures. Client triggers this protocol by deriving the random circular sequencing of such...
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ISBN:
(纸本)9781538636244
The work promotes reliable dynamical validations for transmitted e-documents using session wise randomly hidden secret multi-signatures. Client triggers this protocol by deriving the random circular sequencing of such invisible signatures onto the cover image e-document. Additional security and authenticity criteria met with dynamical orientations of hidden signature bits on concern sub block pixel bytes of the cover image. Vitally both these dynamical fabrications are fully governed by the respective mathematical operations executed on client-server based mutual secret key and session random challenge. Hence, same dynamical authentications for those valid copyright signatures can also be complied at the server end. Apart from that multi-copy signature dispersing with hosting of each individual signature copy on each separate regions of the cover image serves better authenticity and robustness under attacks. Further region wise varied transforms on sub image blocks with variable threshold range driven sign bit encoding on separate transformed pixel bytes of such sub blocks ensures excellent robustness and recovery of signatures. Finally, exhaustive experimental results also confirmed strong superiority of this scheme over existing works with significant enhancements on standard evaluation parameters. Overall, this protocol reflects better trusted authentication, confidentiality and non-repudiation for wireless digital transmissions in contrast to the current ideas.
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