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https://www.readbyqxmd.com/read/28816663/specificity-and-latent-correlation-learning-for-action-recognition-using-synthetic-multi-view-data-from-depth-maps
#1
Bin Liang, Lihong Zheng
This paper presents a novel approach to action recognition using synthetic multi-view data from depth maps. Specifically, multiple views are firstly generated by rotating 3D point clouds from depth maps. A pyramid multi-view depth motion template (MVDMT) is then adopted for multi-view action representation, characterizing the multi-scale motion and shape patterns in 3D. Empirically, despite the view-specific information, the latent information between multiple views often provides important cues for action recognition...
August 14, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28806091/sparsity-based-super-resolution-for-sem-images
#2
Shahar Tsiper, Or Dicker, Idan Kaizerman, Zeev Zohar, Mordechai Segev, Yonina C Eldar
The Scanning Electron Microscope (SEM) is an electron microscope that produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is formed. Since its invention in 1942, the capabilities of SEMs have become paramount in the discovery and understanding of the nanometer world, and today it is extensively used for both research and in industry...
August 14, 2017: Nano Letters
https://www.readbyqxmd.com/read/28796604/dual-temporal-and-spatial-sparse-representation-for-inferring-group-wise-brain-networks-from-resting-state-fmri-dataset
#3
Junhui Gong, Xiaoyan Liu, Tianming Liu, Jiansong Zhou, Gang Sun, Juanxiu Tian
Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. Firstly, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject...
August 9, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28759633/dictionary-learning-based-noisy-image-super-resolution-via-distance-penalty-weight-model
#4
Yulan Han, Yongping Zhao, Qisong Wang
In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained...
2017: PloS One
https://www.readbyqxmd.com/read/28742039/reconstructing-interlaced-high-dynamic-range-video-using-joint-learning
#5
Inchang Choi, Seung-Hwan Baek, Min H Kim
For extending the dynamic range of video, it is a common practice to capture multiple frames sequentially with different exposures and combine them to extend the dynamic range of each video frame. However, this approach results in typical ghosting artifacts due to fast and complex motion in nature. As an alternative, video imaging with interlaced exposures has been introduced to extend the dynamic range. However, the interlaced approach has been hindered by jaggy artifacts and sensor noise, leading to concerns over image quality...
July 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28742035/fast-low-rank-shared-dictionary-learning-for-image-classification
#6
Tiep Huu Vu, Vishal Monga
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries...
July 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708550/unsupervised-myocardial-segmentation-for-cardiac-bold
#7
Ilkay Oksuz, Anirban Mukhopadhyay, Rohan Dharmakumar, Sotirios A Tsaftaris
A fully automated 2D+time myocardial segmentation framework is proposed for Cardiac Magnetic Resonance (CMR) Blood-Oxygen-Level-Dependent (BOLD) datasets. Ischemia detection with CINE BOLD CMR relies on spatiotemporal patterns in myocardial intensity but these patterns also trouble supervised segmentation methods, the de-facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace...
July 12, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28692988/discriminative-transfer-learning-using-similarities-and-dissimilarities
#8
Ying Lu, Liming Chen, Alexandre Saidi, Emmanuel Dellandrea, Yunhong Wang
Transfer learning (TL) aims at solving the problem of learning an effective classification model for a target category, which has few training samples, by leveraging knowledge from source categories with far more training data. We propose a new discriminative TL (DTL) method, combining a series of hypotheses made by both the model learned with target training samples and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary...
July 4, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28692964/joint-semantic-and-latent-attribute-modelling-for-cross-class-transfer-learning
#9
Peixi Peng, Yonghong Tian, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Tiejun Huang
A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge-transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task...
July 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28690670/sparse-representation-based-multi-instance-learning-for-breast-ultrasound-image-classification
#10
Lu Bing, Wei Wang
We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28684331/fiberprint-a-subject-fingerprint-based-on-sparse-code-pooling-for-white-matter-fiber-analysis
#11
Kuldeep Kumar, Christian Desrosiers, Kaleem Siddiqi, Olivier Colliot, Matthew Toews
White matter characterization studies use the information provided by diffusion magnetic resonance imaging (dMRI) to draw cross-population inferences. However, the structure, function, and white matter geometry vary across individuals. Here, we propose a subject fingerprint, called Fiberprint, to quantify the individual uniqueness in white matter geometry using fiber trajectories. We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary. A subject fingerprint is then generated by applying a pooling function for each bundle, thus providing a vector of bundle-wise features describing a particular subject's white matter geometry...
July 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28669918/estimation-of-white-matter-fiber-parameters-from-compressed-multiresolution-diffusion-mri-using-sparse-bayesian-learning
#12
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Julio M Duarte-Carvajalino, Guillermo Sapiro, Christophe Lenglet
We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions...
June 29, 2017: NeuroImage
https://www.readbyqxmd.com/read/28664394/discover-mouse-gene-coexpression-landscapes-using-dictionary-learning-and-sparse-coding
#13
Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Hanchuan Peng, Joe Z Tsien, Tianming Liu
Gene coexpression patterns carry rich information regarding enormously complex brain structures and functions. Characterization of these patterns in an unbiased, integrated, and anatomically comprehensive manner will illuminate the higher-order transcriptome organization and offer genetic foundations of functional circuitry. Here using dictionary learning and sparse coding, we derived coexpression networks from the space-resolved anatomical comprehensive in situ hybridization data from Allen Mouse Brain Atlas dataset...
June 29, 2017: Brain Structure & Function
https://www.readbyqxmd.com/read/28658804/smartphone-the-new-learning-aid-amongst-medical-students
#14
Monika Y Gavali, Deepak S Khismatrao, Yogesh V Gavali, K B Patil
INTRODUCTION: The use of smartphone is increasing day by day for personal as well as professional purpose. They are becoming a more suitable tool for advancing education in developing countries. Mobile access to information and many applications are successfully harnessed in health care. Smartphones are also becoming popular as an effective educational tool. AIM: The present study was conducted to evaluate the use of smartphones as an educational tool amongst the medical students...
May 2017: Journal of Clinical and Diagnostic Research: JCDR
https://www.readbyqxmd.com/read/28644809/structured-kernel-dictionary-learning-with-correlation-constraint-for-object-recognition
#15
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
#16
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
#17
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/28613171/simultaneous-feature-and-dictionary-learning-for-image-set-based-face-recognition
#18
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
#19
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...
July 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28600738/functional-brain-networks-reconstruction-using-group-sparsity-regularized-learning
#20
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
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