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https://www.readbyqxmd.com/read/28644809/structured-kernel-dictionary-learning-with-correlation-constraint-for-object-recognition
#1
Zhengjue Wang, Yinghua Wang, Hongwei Liu, Hao Zhang
In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes...
June 21, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641259/face-hallucination-using-linear-models-of-coupled-sparse-support
#2
Reuben A Farrugia, Christine Guillemot
Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure, hence learn local models on the low-resolution manifold (e.g. sparse or locally linear embedding models), which are then applied on the high- resolution manifold. However, the low-resolution manifold is distorted by the one-to-many relationship between low- and high- resolution patches. This paper presents the Linear Model of Coupled Sparse Support (LM-CSS) method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold...
June 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#3
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28632035/a-comprehensive-study-of-worldwide-selfie-related-accidental-mortality-a-growing-problem-of-the-modern-society
#4
Mohit J Jain, Kinjal J Mavani
Since Oxford dictionary has described 'Selfie', selfie deaths have received a fair amount of coverage but the extent of the problem and the data behind it have not been appropriately explored. The aim of our study is to obtain epidemiological characteristics of selfie-related mortality worldwide with the objective of providing an insight to 'Why selfie', 'Why risky', 'Psychological basis' and 'measures of control.' Despite thousands of web pages, very few scientific articles are available in medical journals...
February 14, 2017: International Journal of Injury Control and Safety Promotion
https://www.readbyqxmd.com/read/28628783/automatic-retrieval-of-shoeprint-images-using-blocked-sparse-representation
#5
Sayyad Alizadeh, Cemal Kose
Shoe marks are regarded as remarkable clues which can be usually detected in crime scenes where forensic experts use them for investigating crimes and identifying the criminals. Hence, developing a robust method for matching shoeprints with one another is of critical significance which can be used for recognizing criminals. In this paper, a promising method is proposed for retrieving shoe marks by means of developing blocking sparse representation technique. In the proposed method, the queried image was divided into two blocks...
June 8, 2017: Forensic Science International
https://www.readbyqxmd.com/read/28613171/simultaneous-feature-and-dictionary-learning-for-image-set-based-face-recognition
#6
Jiwen Lu, Gang Wang, Jie Zhou
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a set of face images which were captured from different variations of pose, illumination, expression, resolution and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa...
June 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28608010/transcriptome-architecture-of-adult-mouse-brain-revealed-by-sparse-coding-of-genome-wide-in-situ-hybridization-images
#7
Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z Tsien, Tianming Liu
Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems...
June 12, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28606869/predicting-mental-conditions-based-on-history-of-present-illness-in-psychiatric-notes-with-deep-neural-networks
#8
Tung Tran, Ramakanth Kavuluru
BACKGROUND: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. OBJECTIVE: We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient's history of present illness typically occurring in the beginning of a psychiatric initial evaluation note...
June 9, 2017: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/28606104/unlocking-echocardiogram-measurements-for-heart-disease-research-through-natural-language-processing
#9
Olga V Patterson, Matthew S Freiberg, Melissa Skanderson, Samah J Fodeh, Cynthia A Brandt, Scott L DuVall
BACKGROUND: In order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study. IMPLEMENTATION: A natural language processing system using a dictionary lookup, rules, and patterns was developed to extract heart function measurements that are typically recorded in echocardiogram reports as measurement-value pairs. Curated semantic bootstrapping was used to create a custom dictionary that extends existing terminologies based on terms that actually appear in the medical record...
June 12, 2017: BMC Cardiovascular Disorders
https://www.readbyqxmd.com/read/28601201/reprint-of-two-stage-sparse-coding-of-region-covariance-via-log-euclidean-kernels-to-detect-saliency
#10
Ying-Ying Zhang, Cai Yang, Ping Zhang
In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel...
August 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28600738/functional-brain-networks-reconstruction-using-group-sparsity-regularized-learning
#11
Qinghua Zhao, Will X Y Li, Xi Jiang, Jinglei Lv, Jianfeng Lu, Tianming Liu
Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction...
June 9, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28600737/extendable-supervised-dictionary-learning-for-exploring-diverse-and-concurrent-brain-activities-in-task-based-fmri
#12
Shijie Zhao, Junwei Han, Xintao Hu, Xi Jiang, Jinglei Lv, Tuo Zhang, Shu Zhang, Lei Guo, Tianming Liu
Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing...
June 9, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28588760/the-impact-of-database-restriction-on-pharmacovigilance-signal-detection-of-selected-cancer-therapies
#13
Manfred Hauben, Eric Hung, Jennifer Wood, Amit Soitkar, Daniel Reshef
BACKGROUND: The aim of this study was to investigate whether database restriction can improve oncology drug pharmacovigilance signal detection performance. METHODS: We used spontaneous adverse event (AE) reports in the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Positive control (PC) drug medical concept (DMC) pairs were selected from safety information not included in the product's first label but subsequently added as label changes...
May 2017: Therapeutic Advances in Drug Safety
https://www.readbyqxmd.com/read/28585946/simultaneous-spectrum-fitting-and-baseline-correction-using-sparse-representation
#14
Quanjie Han, Qiong Xie, Silong Peng, Baokui Guo
Sparse representation has been applied in many domains, such as signal processing, image processing and machine learning. In this paper, a redundant dictionary with each column composed of a Voigt-like lineshape is constructed to represent the pure spectrum of the sample. With the prior knowledge that the baseline is smooth and sparse representation coefficient for a pure spectrum, a method simultaneously fitting the pure spectrum and baseline is proposed. Since the pure spectrum is nonnegative, the representation coefficients are also made to be nonnegative...
June 6, 2017: Analyst
https://www.readbyqxmd.com/read/28585152/overview-of-the-safety-of-anti-vegf-drugs-analysis-of-the-italian-spontaneous-reporting-system
#15
Paola Maria Cutroneo, Claudia Giardina, Valentina Ientile, Simona Potenza, Laura Sottosanti, Carmen Ferrajolo, Costantino J Trombetta, Gianluca Trifirò
INTRODUCTION: Anti-vascular endothelial growth factor (anti-VEGF) drugs are widely used for the treatment of several cancers and retinal diseases. The systemic use of anti-VEGF drugs has been associated with an increased risk of serious adverse reactions. Whether this risk is also related to intravitreal administration of anti-VEGF drugs is unclear. OBJECTIVE: The aim of this study was to provide an overview of the safety of anti-VEGF drugs in oncology and ophthalmology settings using the Italian Spontaneous Reporting System (SRS)...
June 5, 2017: Drug Safety: An International Journal of Medical Toxicology and Drug Experience
https://www.readbyqxmd.com/read/28583204/building-a-semantic-web-based-metadata-repository-for-facilitating-detailed-clinical-modeling-in-cancer-genome-studies
#16
Deepak K Sharma, Harold R Solbrig, Cui Tao, Chunhua Weng, Christopher G Chute, Guoqian Jiang
BACKGROUND: Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains...
June 5, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/28578176/research-standardization-tools-pregnancy-measures-in-the-phenx-toolkit
#17
Ann Kinga Malinowski, Cande V Ananth, Patrick Catalano, Erin P Hines, Russell S Kirby, Mark A Klebanoff, John J Mulvihill, Hyagriv Simhan, Carol M Hamilton, Tabitha P Hendershot, Michael J Phillips, Lisa A Kilpatrick, Deborah R Maiese, Erin M Ramos, Rosalind J Wright, Siobhan M Dolan
Only through concerted and well-executed research endeavors can we gain the requisite knowledge to advance pregnancy care and have a positive impact on maternal and newborn health. Yet the heterogeneity inherent in individual studies limits our ability to compare and synthesize study results, thus impeding the capacity to draw meaningful conclusions that can be trusted to inform clinical care. The PhenX Toolkit (http://www.phenxtoolkit.org), supported since 2007 by the National Institutes of Health, is a web-based catalog of standardized protocols for measuring phenotypes and exposures relevant for clinical research...
May 31, 2017: American Journal of Obstetrics and Gynecology
https://www.readbyqxmd.com/read/28574357/single-image-rain-streak-separation-using-layer-priors
#18
Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, Michael S Brown
Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision and image analysis systems. Rain streak removal algorithms try to recover a rain streak free background scene. In this paper, we address the problem of rain streak removal from a single image by formulating it as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either sparse dictionary learning methods or impose a low rank structure on the appearance of the rain streaks...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28574352/a-hierarchical-approach-for-rain-or-snow-removing-in-a-single-color-image
#19
Yinglong Wang, Shuaicheng Liu, Chen Chen, Bing Zeng
In this paper, we propose an efficient algorithm to remove rain or snow from a single color image. Our algorithm takes advantage of two popular techniques employed in image processing, namely, image decomposition and dictionary learning. At first, a combination of rain/snow detection and a guided filter is used to decompose the input image into a complementary pair: (1) the low-frequency part that is free of rain or snow almost completely and (2) the high-frequency part that contains not only the rain/snow component but also some or even many details of the image...
May 26, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28564686/bayesian-depth-estimation-from-monocular-natural-images
#20
Che-Chun Su, Lawrence K Cormack, Alan C Bovik
Estimating an accurate and naturalistic dense depth map from a single monocular photographic image is a difficult problem. Nevertheless, human observers have little difficulty understanding the depth structure implied by photographs. Two-dimensional (2D) images of the real-world environment contain significant statistical information regarding the three-dimensional (3D) structure of the world that the vision system likely exploits to compute perceived depth, monocularly as well as binocularly. Toward understanding how this might be accomplished, we propose a Bayesian model of monocular depth computation that recovers detailed 3D scene structures by extracting reliable, robust, depth-sensitive statistical features from single natural images...
May 1, 2017: Journal of Vision
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