Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity togethe...
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ISBN:
(纸本)9781479983926
Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.
Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters ...
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With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes ...
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With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes developed for these videos mostly encode the background as the long-term reference (LTR) to further improve the coding efficiency. However, since the bit allocation of the long-term background reference is not intensively studied, the coding efficiency is still unsatisfactory. Based on the stability analysis of the video content, an efficient background picture coding algorithm for videos obtained from static cameras, which is embedded with the basic unit level bit allocation, is proposed in this paper. Experimental results reveal that on top of the default mode in HEVC, our method offers the performance with 10.8% BD-rate reduction on average. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences with negligible increases of computational complexity in both encoder and decoder.
Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control system. This paper studies the stochastic stability of a affine nonlinear system within random ...
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ISBN:
(纸本)9781467374439
Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control system. This paper studies the stochastic stability of a affine nonlinear system within random time delay. The system controller is consist of a control prediction generator(CPG) at the controller side and a network delay compensator(NDC) at the plant side. Different from the previous literatures that model the random delay of the feedback channel and the forward channel as Markov chain respectively, the model in this paper is based on the round-trip delay, which making the predictive control process more concise but without losing generality. Then, this paper gives the necessary and sufficient conditions for stochastic stability of the closed-loop system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.
This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise contin...
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Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control system. This paper studies the stochastic stability of a affine nonlinear system within random ...
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In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means cluste...
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In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approache...
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In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.
Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing fea...
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This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynami...
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This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM). However, Kleinberg's model is originally proposed for the burst of keywords, the frequency counts it models are not proper to describe the burst states of topics, leading to some unwanted results. A more reasonable way is to model the influence burst states put on each document's topic distribution. Considering this, we propose a unified statistical model that takes the burst states as markov latent variables that influence the topic allocation of documents. We derive a Gibbs sampling algorithm for the proposal. Experiment results confirm our model's advantages both qualitatively and quantitatively.
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