This paper presents a novel approach to visual object tracking based on particle filtering. The tracked object is modelled by a sparse representation provided by dictionary learning. Such an approach permits to descri...
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ISBN:
(纸本)9781479983407
This paper presents a novel approach to visual object tracking based on particle filtering. The tracked object is modelled by a sparse representation provided by dictionary learning. Such an approach permits to describe the target by a model of reduced dimension. The likelihood of a candidate region is built on a similarity measure between the sparse representations of a set of patches (at known positions) in the dictionary learnt from the reference template. Experimental validation is performed on various video sequences and shows the robustness of the proposed approach.
With the development of society, the image size and resolution are gradually increasing. At the same time, the fast image processing is required. The K-SVD algorithm is an iterative method that alternates between spar...
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With the development of society, the image size and resolution are gradually increasing. At the same time, the fast image processing is required. The K-SVD algorithm is an iterative method that alternates between sparse coding and updating the overcomplete dictionary. It is a very simple and highly effective method for the signal of sparse representation. However, the K-SVD algorithm is a computationally intensive algorithm. The traditional computing model will take a lot of time to complete the calculation. As the next generation computing model, Spark not only has a strong computing power, but also has a memory-based fast processing capacity. In order to process image denoising efficiently, this paper proposes a implementation of distributed parallel optimization of K-SVD algorithm(K-SVD-P) on Spark. The results showed that K-SVDP not only has a good speed-up ratio, but also retains the image texture and other details.
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by usi...
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ISBN:
(纸本)9781538646595
Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.
Non-rigid 3D shape recognition is an important and challenging research topic in computer vision and pattern recognition. This paper presents a novel algorithm, called dictionary learning based on supervised locally l...
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ISBN:
(纸本)9781479903573
Non-rigid 3D shape recognition is an important and challenging research topic in computer vision and pattern recognition. This paper presents a novel algorithm, called dictionary learning based on supervised locally linear representation (DL-SLLR), for efficient 3D shape recognition using shape descriptors. Specifically, we introduce a novel locality-preservation error term along with a label approximation error term into the objective function. The proposed algorithm optimizes a dictionary for its capability in representation as well as its locality-preservation capability, which thus allows more consistent encoding of similar descriptors compared with sparse coding. In addition, the proposed SLLR coding yields a closed-form solution, compared to many sparse coding algorithms. Experimental results demonstrate that using majority voting, DL-SLLR outperforms D-KSVD and SVM over a newly generated SLI 3D Face Dataset and the SHREC'11 Contest Dataset.
We address the problem of denoising for image patches. The approach taken is based on Bayesian modeling of sparse representations, which takes into account dependencies between the dictionary atoms. Following recent w...
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ISBN:
(纸本)9781457705380
We address the problem of denoising for image patches. The approach taken is based on Bayesian modeling of sparse representations, which takes into account dependencies between the dictionary atoms. Following recent work, we use a Boltzman machine to model the sparsity pattern. In this work we focus on the special case of a unitary dictionary and obtain the exact MAP estimate for the sparse representation using an efficient message passing algorithm. We present an adaptive model-based scheme for sparse signal recovery, which is based on sparse coding via message passing and on learning the model parameters from the data. This adaptive approach is applied on noisy image patches in order to recover their sparse representations over a fixed unitary dictionary. We compare the denoising performance to that of previous sparse recovery methods, which do not exploit the statistical dependencies, and show the effectiveness of our approach.
Bidirectional Associative Memories (BAMs) are artificial neural networks that can learn and recall various types of associations. Although BAM models have shown great promise at modeling human cognitive processes, the...
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ISBN:
(纸本)9781509006212
Bidirectional Associative Memories (BAMs) are artificial neural networks that can learn and recall various types of associations. Although BAM models have shown great promise at modeling human cognitive processes, these models have often been investigated under conditions where stimuli are densely represented using a bipolar coding scheme. However, research has shown that dense representations are energetically costly given that various stimulus representations need to be detected, processed and analyzed on a daily basis. Instead, biological networks work on minimizing energy expenditure by encodingsparse stimulus features that maximize information representation. This paper extends this line of search and shows that BAM models can improve learning and recall performance in a sparse encoding regime. It provides a strategy for artificial neural networks that seek to maintain valuable processing resources, especially under constraints of noisy representations of stimulus features.
In this work, we consider enhancing a target speech from a single-channel noisy observation corrupted by non-stationary noises at low signal-to-noise ratios (SNRs). We take a classification-based approach, where the o...
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ISBN:
(纸本)9781467369985
In this work, we consider enhancing a target speech from a single-channel noisy observation corrupted by non-stationary noises at low signal-to-noise ratios (SNRs). We take a classification-based approach, where the objective is to estimate an Ideal Binary Mask (IBM) that classifies each time-frequency (T-F) unit of the noisy observation into one of the two categories: speech-dominant unit or noise-dominant unit. The estimated mask is used to binary weight the noisy mixture to obtain the enhanced speech. In the proposed system, the sparse non-negative matrix factorization (NMF) is used to extract features from the noisy observation, followed by a Deep Neural Network (DNN) for classification. Compared with several existing classification-based systems, the proposed system uses minimal speech-specific domain knowledge, but is able to achieve better performance in certain low SNR regions. Moreover, the proposed system outperforms the traditional statistical method, especially in terms of improving the intelligibility.
Coolant system is one of the most important systems in nuclear power plant. The safety and stability of the system is the basis of the stable operation of the whole nuclear power unit. Firstly, the typical fault chara...
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Coolant system is one of the most important systems in nuclear power plant. The safety and stability of the system is the basis of the stable operation of the whole nuclear power unit. Firstly, the typical fault characteristics of the system are introduced. In order to improve the accuracy of fault diagnosis, an expert diagnosis method based on sparse coding is proposed by fusing the sparse coding fault diagnosis results with the expert system. The results show that the method can extract fault feature information effectively. The accuracy of expert system for fault diagnosis of coolant system is 87.5%. After sparse coding fusion, the accuracy of fault diagnosis is increased to 94.4%. The validity and reliability of the system are verified, which can provide reference for safe operation and decision-making of nuclear power plant.
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a ...
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ISBN:
(纸本)9781467399623
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode, in which the algorithm employs a training set to learn the dictionary and linear classifier simultaneously, and the online semi-supervised learning mode, in which the algorithm continuously learns as it classifies new vectors. Experiments performed on two face recognition databases demonstrate that the proposed method outperforms state-of-the-art sparse coding algorithms.
O-glycosylation is one of the main types of the mammalian protein glycosylation, it occurs on the particular site of serine and threonine. It's important to predict the O-glycosylation site. In this paper, we prop...
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O-glycosylation is one of the main types of the mammalian protein glycosylation, it occurs on the particular site of serine and threonine. It's important to predict the O-glycosylation site. In this paper, we propose a new method of kernel principal component analysis(KPCA) to predict the Oglycosylation site with window size w=9. The samples for experiment are encoded by the sparse coding and projected into kernel space first, then the features are extracted by PCA, at last the classification is done by Mahanalobis distance. The result of experiments shows that the proposed method of KPCA is more effective and accurate than PCA. The prediction accuracy is about 84.5%.
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