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https://www.readbyqxmd.com/read/28092688/buckled-two-dimensional-xene-sheets
#1
Alessandro Molle, Joshua Goldberger, Michel Houssa, Yong Xu, Shou-Cheng Zhang, Deji Akinwande
Silicene, germanene and stanene are part of a monoelemental class of two-dimensional (2D) crystals termed 2D-Xenes (X = Si, Ge, Sn and so on) which, together with their ligand-functionalized derivatives referred to as Xanes, are comprised of group IVA atoms arranged in a honeycomb lattice - similar to graphene but with varying degrees of buckling. Their electronic structure ranges from trivial insulators, to semiconductors with tunable gaps, to semi-metallic, depending on the substrate, chemical functionalization and strain...
January 16, 2017: Nature Materials
https://www.readbyqxmd.com/read/28092596/a-general-framework-of-dynamic-constrained-multiobjective-evolutionary-algorithms-for-constrained-optimization
#2
Sanyou Zeng, Ruwang Jiao, Changhe Li, Xi Li, Jawdat S Alkasassbeh
A novel multiobjective technique is proposed for solving constrained optimization problems (COPs) in this paper. The method highlights three different perspectives: 1) a COP is converted into an equivalent dynamic constrained multiobjective optimization problem (DCMOP) with three objectives: a) the original objective; b) a constraint-violation objective; and c) a niche-count objective; 2) a method of gradually reducing the constraint boundary aims to handle the constraint difficulty; and 3) a method of gradually reducing the niche size aims to handle the multimodal difficulty...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092591/relational-regularized-discriminative-sparse-learning-for-alzheimer-s-disease-diagnosis
#3
Baiying Lei, Peng Yang, Tianfu Wang, Siping Chen, Dong Ni
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092588/a-many-objective-evolutionary-algorithm-using-a-one-by-one-selection-strategy
#4
Yiping Liu, Dunwei Gong, Jing Sun, Yaochu Jin
Most existing multiobjective evolutionary algorithms experience difficulties in solving many-objective optimization problems due to their incapability to balance convergence and diversity in the high-dimensional objective space. In this paper, we propose a novel many-objective evolutionary algorithm using a one-by-one selection strategy. The main idea is that in the environmental selection, offspring individuals are selected one by one based on a computationally efficient convergence indicator to increase the selection pressure toward the Pareto optimal front...
January 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092585/saliency-based-lesion-segmentation-via-background-detection-in-dermoscopic-images
#5
Euijoon Ahn, Jinman Kim, Lei Bi, Ashnil Kumar, Changyang Li, Michael Fulham, Dagan Feng
The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis (CAD) of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and over-segmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions...
January 16, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092573/environment-sensitivity-based-cooperative-co-evolutionary-algorithms-for-dynamic-multi-objective-optimization
#6
Biao Xu, Yong Zhang, Dunwei Gong, Yinan Guo, Miao Rong
Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092559/learning-to-hash-with-optimized-anchor-embedding-for-scalable-retrieval
#7
Yuchen Guo, Guiguang Ding, Li Liu, Jungong Han, Ling Shao
Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., Sparse Hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (highdimensional) image features into a low-dimensional Hamming space while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092558/discriminant-context-information-analysis-for-post-ranking-person-re-identification
#8
Jorge Garcia, Niki Martinel, Alfredo Gardel, Ignacio Bravo, Gian Luca Foresti, Christian Micheloni
Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092557/single-image-super-resolution-using-global-regression-based-on-multiple-local-linear-mappings
#9
Jae-Seok Choi, Munchurl Kim
Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition (FHD) input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between PSNR performances and computational complexity...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092554/graph-laplacian-regularization-for-image-denoising-analysis-in-the-continuous-domain
#10
Jiahao Pang, Gene Cheung
Inverse imaging problems are inherently underdetermined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior- the graph Laplacian regularizer-assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092552/discriminative-elastic-net-regularized-linear-regression
#11
Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092551/hierarchical-image-segmentation-based-on-iterative-contraction-and-merging
#12
Jia-Hao Syu, Sheng-Jyh Wang, Li-Chun Wang
In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging (ICM). In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092549/learning-short-binary-codes-for-large-scale-image-retrieval
#13
Li Liu, Mengyang Yu, Ling Shao
Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/ binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092548/linear-spectral-clustering-superpixel
#14
Jiansheng Chen, Zhengqin Li, Bo Huang
In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the space proximity between image pixels. However, rather than directly using the traditional eigen-based algorithm, we approximate the similarity metric through a deliberately designed kernel function such that pixel values can be explicitly mapped to a high-dimensional feature space...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092546/geometry-guided-multi-scale-depth-map-fusion-via-graph-optimization
#15
Pengfei Wu, Yiguang Liu, Mao Ye, Zhenyu Xu, Yunan Zheng
In depth discontinuous and untextured regions, depth maps created by multiple view stereopsis are with heavy noises, but existing depth map fusion methods cannot handle it explicitly. To tackle the problem, two novel strategies are proposed: 1) a more discriminative fusion method, which is based on geometry consistency, measuring the consistency and stability of surface geometry computed on both partial and global surfaces, different from traditional methods only using visibility consistency; 2) a graph optimization method which fuses pyramids of depth maps as mutual complementary information is available in different scales, and differs from existing multiscale fusion methods...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092544/discriminative-multi-view-interactive-image-re-ranking
#16
Jun Li, Chang Xu, Wankou Yang, Changyin Sun, Dacheng Tao
-Given unreliable visual patterns and insufficient query information, content-based image retrieval (CBIR) is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose Discriminative Multi-view INTeractive Image Re-ranking (DMINTIR), which integrates User Relevance Feedback (URF) capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092543/a-robust-wrap-reduction-algorithm-for-fringe-projection-profilometry-and-applications-in-magnetic-resonance-imaging
#17
Miguel Arevalillo-Herraez, Maximo Cobos, Miguel Garcia Pineda
In this paper, we present an effective algorithm to reduce the number of wraps in a two-dimensional (2-D) phase signal provided as input. The technique is based on an accurate estimate of the fundamental frequency of a 2-D complex signal with the phase given by the input, and the removal of a dependent additive term from the phase map. Unlike existing methods based on the Discrete Fourier Transform (DFT), the frequency is computed by using noise-robust estimates that are not restricted to integer values. Then, to deal with the problem of a non-integer shift in the frequency domain, an equivalent operation is carried out on the original phase signal...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092542/bayesian-face-sketch-synthesis
#18
Nannan Wang, Xinbo Gao, Leiyu Sun, Jie Li
xemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical modelsxemplar-based face sketch synthesis has been widely applied to both digital entertainment and law enforcement. In this paper, we propose a Bayesian framework for face sketch synthesis, which provides a systematic interpretation for understanding the common properties and intrinsic difference in different methods from the perspective of probabilistic graphical modelsE...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092539/object-based-multiple-foreground-segmentation-in-rgbd-video
#19
Huazhu Fu, Dong Xu, Stephen Lin
We present an RGBD video segmentation method that takes advantage of depth data and can extract multiple foregrounds in the scene. This video segmentation is addressed as an object proposal selection problem formulated in a fullyconnected graph where a flexible number of foregrounds may be chosen. In the graph, each node represents a proposal, and the edges model intra-frame and inter-frame constraints on the solution. The proposals are generated based on an RGBD video saliency map in which depth-based features are utilized to enhance identification of foregrounds...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092530/feasibility-of-multi-plane-transmit-beamforming-for-real-time-volumetric-cardiac-imaging-a-simulation-study
#20
Yinran Chen, Ling Tong, Alejandra Ortega, Jianwen Luo, Jan D'hooge
Today's three-dimensional (3-D) cardiac ultrasound imaging systems suffer from relatively low spatial and temporal resolution, limiting their applicability in daily clinical practice. To address this problem, 3-D diverging wave imaging with spatial coherent compounding (DWC) as well as 3-D multi-line-transmit (MLT) imaging have recently been proposed. Currently, the former improves the temporal resolution significantly at the expense of image quality and the risk of introducing motion artifacts whereas the latter only provides a moderate gain in volume rate but mostly preserves quality...
January 10, 2017: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
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