Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face r...
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Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face imagerecognition.
Aiming at adverse influence of the correlation between measurement and process noise for filtering precision, a new multiple model particle filtering algorithm with correlated measurement noise and process noise is pr...
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Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordin...
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Given a smooth surface that is z-axis symmetric, illuminated in an arbitrary direction and fully contained within the field of view, it is proven that shape-from-shading is uniquely determined by using a polar coordinate system and a Fourier expansion.
A new model-based speech enhancement algorithm by variational Bayesian learning was proposed in this paper. Autoregressive process was used to model speech signal and its order was determined automatically. Clean spee...
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A new model-based speech enhancement algorithm by variational Bayesian learning was proposed in this paper. Autoregressive process was used to model speech signal and its order was determined automatically. Clean speech signal could be estimated using a variational Kalman smoother. Moreover, overfitting was avoided in the learning of model parameter and model structure. Experimental results compared with Kalman filter-based enhancement and spectral subtraction methods demonstrate the performance of our algorithm.
An additive discussion for the validity of using the weighted information entropy to evaluate the complex degree of infrared （IR） backgronnds is given, Since small targets can be temporarily lost in actual infrared ...
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An additive discussion for the validity of using the weighted information entropy to evaluate the complex degree of infrared （IR） backgronnds is given, Since small targets can be temporarily lost in actual infrared video sequences, an adaptive binarization threshold for small targets detection is presented. Experimental results show the robustness of our method,
The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement a...
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Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasing...
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Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.
In this paper, we try to deal with the problem of shadow detection from static images and video sequences. In instead to considering individual regions separately, we use relative illumination conditions between segme...
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This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff pr...
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This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient （HTffOG） feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.
The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensio...
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The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensional signals in the sense that exact reconstruction of the waveform requires a lower sampling density than with the alternative schemes. In this Letter, a hexagonal discrete cosine transform (HDCT) for encoding the hexagonally sampled signals is presented.
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