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Highlights: [•] An efficient dictionary learning method is proposed. [•] Fast kNN graph construction is well integrated with submodular dictionary learning. [•] The proposed method finds a balance between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose a novel target-oriented shape prior modeling method. [•] Measure the intrinsic similarity between the target shape and the training shapes. [•] Incorporate the shape model into an optimized search based segmentation method. [•] Exhibit better performance than other existing methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] Classical GVF based algorithms face challenges such as over- or under-segmentation. [•] Our MSGVF algorithm pursues a minimised combination of mean shift and GVF terms. [•] The proposed algorithm is evaluated against two publicly accessible databases. [•] MSGVF achieves better segmentation performance than the classical methods. [ABSTRACT FROM AUTHOR]

Abstract: The proportion of aurora to the field-of-view in temporal series of all-sky images is an important index to investigate the evolvement of aurora. To obtain such an index, a crucial phase is to segment the aurora from the background of sky. A new aurora segmentation approach, including a feature extraction method and the segmentation algorithm, is presented in this paper. The proposed feature extraction method, called adaptive local binary patterns (ALBP), selects the frequently occurred patterns to construct the main pattern set, which avoids using the same pattern set to describe different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of aurora, the segmentation algorithm is designed into two parts, texture part segmentation based on ALBP features and patch part segmentation based on modified Otsu method. As it is simple and efficient, our implementation is suitable for large-scale datasets. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy. [Copyright &y& Elsevier]

Abstract: This paper presents a novel framework for effective video semantic analysis. This framework has two major components, namely, optical flow tensor (OFT) and hidden Markov models (HMMs). OFT and HMMs are employed because: (1) motion is one of the fundamental characteristics reflecting the semantic information in video, so an OFT-based feature extraction method is developed to make full use of the motion information. Thereafter, to preserve the structure and discriminative information presented by OFT, general tensor discriminant analysis (GTDA) is used for dimensionality reduction. Finally, linear discriminant analysis (LDA) is utilized to further reduce the feature dimension for discriminative motion information representation; and (2) video is a sort of information intensive sequential media characterized by its context-sensitive nature, so the video sequences can be more effectively analyzed by some temporal modeling tools. In this framework, we use HMMs to well model different levels of semantic units (SU), e.g., shot and event. Experimental results are reported to demonstrate the advantages of the proposed framework upon semantic analysis of basketball video sequences, and the cross validations illustrate its feasibility and effectiveness. [Copyright &y& Elsevier]

Abstract: With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. [Copyright &y& Elsevier]

Background/objective Vascular endothelial growth factor (VEGF) has been recognized to be a potential pharmaceutical target for treating ischemic stroke, but its severe side effects hinder its widely application. Here, the present study was designed to investigate the effects of VEGF on blood-brain-barrier (BBB) disruption and the underlying mechanisms. Methods A mouse model of middle cerebral artery occlusion (MCAO) was constructed and treated with or without VEGF. Meanwhile, mice brain microvascular endothelial cells in co-culture with astrocytes were subjected to 1, 2 and 4 h oxygen–glucose deprivation followed by 24 h of reperfusion (OGD/R) in the absence or presence of VEGF. The mRNA and protein expression were assessed by real-time PCR and Western blotting. Fluorescence in situ hybridization (FISH) was utilized to validate LOC102640519 expression in OGD/R cell models. Chromatin Immunoprecipitation (ChIP) assay was used to confirm the regulatory mechanism of LOC102640519 to HOXC13. Interactions between HOXC13 and ZO-1 were measured by a luciferase reporter assay and RNA pull down assay. Results Our results showed that administration of VEGF significantly aggravated BBB by upregulating LOC102640519 and HOXC13 expression in vitro and vitro model of cerebral ischemia. Furthermore, LOC102640519 positively regulated the expression of HOXC13, thus negatively regulated the expression of ZO-1, Occludin and Claudin-5 in OGD/R model in the absence or presence of VEGF. Conclusions VEGF aggravated BBB disruption after cerebral I/R-induced injury probably by increasing LOC102640519 and HOXC13 through inhibition of ZO-1, Occludin and Claudin-5. [ABSTRACT FROM AUTHOR]

Abstract: Based on the assumption about the sparse representation of natural images and the theory of compressed sensing, very promising results about single-image super-resolution were obtained by an excellent algorithm introduced by Yang et al. [45]. However, their success could not be well explained theoretically. The lack of theoretical insight has hindered the further improvement of the algorithm. In this paper, Yang’s algorithm is revisited in the view of learning theory. According to this point, Yang’s algorithm can be considered as a linear regression method in a special feature space which is named as sparse coding space by us. In fact, it has been shown that Yang’s algorithm is a result of optimal linear estimation in sparse coding space. More importantly, our theoretical analysis suggests that Yang’s algorithm can be improved by using more flexible regression methods than the linear regression method. Following the idea, a novel single-image super-resolution algorithm which is designed based on the framework of L 2-Boosting is proposed in the paper. The experimental results show the effectiveness of the proposed algorithm by comparing with other methods, which verify our theoretical analysis about Yang’s algorithm. [Copyright &y& Elsevier]