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https://www.readbyqxmd.com/read/28323824/local-structure-preserving-sparse-coding-for-infrared-target-recognition
#1
Jing Han, Jiang Yue, Yi Zhang, Lianfa Bai
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images...
2017: PloS One
https://www.readbyqxmd.com/read/28316615/a-robust-shape-reconstruction-method-for-facial-feature-point-detection
#2
Shuqiu Tan, Dongyi Chen, Chenggang Guo, Zhiqi Huang
Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28315459/dynamic-reorganization-of-intrinsic-functional-networks-in-the-mouse-brain
#3
Joanes Grandjean, Maria Giulia Preti, Thomas Aw Bolton, Michaela Buerge, Erich Seifritz, Christopher R Pryce, Dimitri Van De Ville, Markus Rudin
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions...
March 14, 2017: NeuroImage
https://www.readbyqxmd.com/read/28269050/dictionary-learning-for-sparse-representation-and-classification-of-neural-spikes
#4
Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose sub-dictionaries are class specific, and estimate discriminative sparse coding coefficients by minimizing the within class scatter and maximizing the between class scatter...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268573/deep-neural-ensemble-for-retinal-vessel-segmentation-in-fundus-images-towards-achieving-label-free-angiography
#5
A Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268558/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#6
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28267565/automating-cell-detection-and-classification-in-human-brain-fluorescent-microscopy-images-using-dictionary-learning-and-sparse-coding
#7
Maryana Alegro, Panagiotis Theofilas, Austin Nguy, Patricia A Castruita, William Seeley, Helmut Heinsen, Daniela M Ushizima, Lea T Grinberg
BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility...
March 4, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28252417/greedy-criterion-in-orthogonal-greedy-learning
#8
Lin Xu, Shaobo Lin, Jinshan Zeng, Xia Liu, Yi Fang, Zongben Xu
Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts with selecting a new atom from a specified dictionary via the steepest gradient descent (SGD) and then builds the estimator through orthogonal projection. In this paper, we found that SGD is not the unique greedy criterion and introduced a new greedy criterion, called as ''δ-greedy threshold'' for learning. Based on this new greedy criterion, we derived a straightforward termination rule for OGL. Our theoretical study shows that the new learning scheme can achieve the existing (almost) optimal learning rate of OGL...
February 23, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28251192/learning-based-topological-correction-for-infant-cortical-surfaces
#9
Shijie Hao, Gang Li, Li Wang, Yu Meng, Dinggang Shen
Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However, due to rapid growth and ongoing myelination, infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns, thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results, in comparison to adult MR images which typically have good tissue contrast...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28248370/multi-focus-image-fusion-based-on-dictionary-learning-with-rolling-guidance-filter
#10
Xiang Yan, Hanlin Qin, Jia Li
We present a new multi-focus image fusion method based on dictionary learning with a rolling guidance filter to fusion of multi-focus images with registration and mis-registration. First, we learn a dictionary via several classical multi-focus images blurred by a rolling guidance filter. Subsequently, we present a new model for focus regions identification via applying the learned dictionary to input images to obtain the corresponding focus feature maps. Then, we determine the initial decision map via comparing the difference of the focus feature maps...
March 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
https://www.readbyqxmd.com/read/28242473/task-fmri-data-analysis-based-on-supervised-stochastic-coordinate-coding
#11
Jinglei Lv, Binbin Lin, Qingyang Li, Wei Zhang, Yu Zhao, Xi Jiang, Lei Guo, Junwei Han, Xintao Hu, Christine Guo, Jieping Ye, Tianming Liu
Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks...
February 20, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28240216/training-free-compressed-sensing-for-wireless-neural-recording-using-analysis-model-and-group-weighted-l1-minimization
#12
Biao Sun, Wenfeng Zhao, Xinshan Zhu
OBJECTIVE: Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and Compressed Sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed Group Weighted Analysis l1-Minimization (GWALM), is proposed for wireless neural recording. APPROACH: The GWALM method consists of three parts: 1) The analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance...
February 27, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28227266/dictionary-learning-for-sparse-representation-and-classification-of-neural-spikes
#13
Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Ahmed H Dallal, Yiran Chen, Douglas Weber, Zhi-Hong Mao, Douglas Weber, Zhi-Hong Mao, Yiran Chen, Ahmed H Dallal
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose sub-dictionaries are class specific, and estimate discriminative sparse coding coefficients by minimizing the within class scatter and maximizing the between class scatter...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226751/deep-neural-ensemble-for-retinal-vessel-segmentation-in-fundus-images-towards-achieving-label-free-angiography
#14
A Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas, A Lahiri, Abhijit Guha Roy, Debdoot Sheet, Prabir Kumar Biswas, Debdoot Sheet, Prabir Kumar Biswas, A Lahiri
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#15
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28214787/sparse-and-dense-hybrid-representation-via-subspace-modeling-for-dynamic-mri
#16
Qiegen Liu, Shanshan Wang, Dong Liang
Recent theoretical results on compressed sensing and low-rank matrix recovery have inspired significant interest in joint sparse and low rank modeling of dynamic magnetic resonance imaging (dMRI). Existing approaches usually describe these two respective prior information with different formulations. In this paper, we present a novel sparse and dense hybrid representation (SDR) model which describes the sparse plus low rank properties by a unified way. More specifically, under the learned dictionary consisting of temporal basis functions, SDR models the spatial coefficients in two subspaces with Laplacian and Gaussian prior distributions, respectively...
February 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28212079/multi-view-multi-instance-learning-based-on-joint-sparse-representation-and-multi-view-dictionary-learning
#17
Bing Li, Chunfeng Yuan, Weihua Xiong, Weiming Hu, Houwen Peng, Xinmiao Ding, Stephen Maybank
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M2IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse "-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL...
February 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28187898/vessel-segmentation-and-microaneurysm-detection-using-discriminative-dictionary-learning-and-sparse-representation
#18
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28182553/multi-dimensional-sparse-models
#19
Na Qi, Yunhui Shi, Xiaoyan Sun, Jingdong Wang, Baocai Yin, Junbin Gao
Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize the multilinearity of tensors to establish the redundant basis of the space of multi linear maps with the sparsity constraint, and further propose MD synthesis/analysis sparse models to effectively and efficiently represent MD signals in their original form...
February 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28129152/a-new-sparse-representation-framework-for-reconstruction-of-an-isotropic-high-spatial-resolution-mr-volume-from-orthogonal-anisotropic-resolution-scans
#20
Yuanyuan Jia, Ali Gholipour, Zhongshi He, Simon Warfield
In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution 3D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multi-frame super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the high-resolution slices and the low-resolution sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of over-complete dictionaries that are used in a sparse land local model to upsample the input scans...
January 23, 2017: IEEE Transactions on Medical Imaging
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