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hyperspectral imaging (42) 42
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21. Full Text Set-to-Set Distance-Based Spectral-Spatial Classification of Hyperspectral Images
by Ting Lu and Shutao Li and Leyuan Fang and Bruzzone, Lorenzo and Benediktsson, Jon Atli
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 12/2016, Volume 54, Issue 12, pp. 7122 - 7134
A novel set-to-set distance-based spectral-spatial classification method for hyperspectral images (HSIs) is proposed. In HSIs, the spatially connected and... 
Training | hyperspectral image (HSI) | Image segmentation | Adaptation models | Image edge detection | Affine hull (AH) representation | set-to-set distance | Kernel | spectral–spatial classification | Hyperspectral imaging | spectral-spatial classification | SUPPORT VECTOR MACHINES | FEATURE-EXTRACTION | REMOTE-SENSING IMAGES | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | SPARSE REPRESENTATION | FRAMEWORK
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22. Full Text Adaptive Spectral-Spatial Compression of Hyperspectral Image With Sparse Representation
by Wei Fu and Shutao Li and Leyuan Fang and Benediktsson, Jon Atli
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 02/2017, Volume 55, Issue 2, pp. 671 - 682
Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into sparse coefficients with very few nonzero entries, which can... 
hyperspectral image (HSI) | sparse representation (SR) | Image coding | Dictionaries | Compression | Transform coding | Encoding | superpixel | Discrete wavelet transforms | Hyperspectral imaging | JPEG2000 | APPROXIMATION | REMOTE-SENSING IMAGES | CLASSIFICATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | COLLABORATIVE REPRESENTATION | ALGORITHMS | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | RECOVERY | REMOTE SENSING | State of the art | Methodology | Spectral signatures | Spatial discrimination | Distortion | Image compression | Entropy | Spectra | Regions | Coefficients | Pixels | Spatial data | Coding | Representations
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23. Full Text Hyperspectral Image Classification Via Shape-Adaptive Joint Sparse Representation
by Wei Fu and Shutao Li and Leyuan Fang and Xudong Kang and Benediktsson, Jon Atli
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, 02/2016, Volume 9, Issue 2, pp. 556 - 567
A new shape-adaptive joint sparse representation classification (SAJSRC) method is proposed for hyperspectral images (HSIs) classification. The proposed method... 
Support vector machines | Training | hyperspectral image (HSI) | Image edge detection | Classification | shape-adaptive algorithm | Sparse matrices | Joints | Hyperspectral imaging | Principal component analysis | sparse representation | shapeadaptive algorithm | COLLABORATIVE-REPRESENTATION | GEOGRAPHY, PHYSICAL | REMOTE SENSING | APPROXIMATION | REDUCTION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Principal components analysis | Classifiers | Adaptive structures | Algorithms | Representations | Pixels | Image classification
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24. Full Text Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation
by Leyuan Fang and Shutao Li and McNabb, Ryan P and Qing Nie and Kuo, Anthony N and Toth, Cynthia A and Izatt, Joseph A and Farsiu, Sina
IEEE Transactions on Medical Imaging, ISSN 0278-0062, 11/2013, Volume 32, Issue 11, pp. 2034 - 2049
In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our... 
Training | Interpolation | Dictionaries | Image resolution | image enhancement | Noise reduction | optical coherence tomography | Tomography | simultaneous denoising and interpolation | Image reconstruction | Fast retina scanning | sparse representation | SIGNAL | SUPERRESOLUTION | ENGINEERING, BIOMEDICAL | DECOMPOSITION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | INTERPOLATION | RECOVERY | SD-OCT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | AUTOMATIC SEGMENTATION | NOISE-REDUCTION | ENHANCEMENT | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | DICTIONARIES | Algorithms | Animals | Optic Nerve - anatomy & histology | Humans | Mice | Image Processing, Computer-Assisted - methods | Tomography, Optical Coherence - methods | Retina - pathology | Macular Degeneration - pathology | Retina - anatomy & histology | Research | Image processing | Analysis | Optical tomography | Coherence (Optics) | Sparsity | Reconstruction | Optical Coherence Tomography | Imaging | Images | Representations
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25. Full Text Extended Random Walker-Based Classification of Hyperspectral Images
by Xudong Kang and Shutao Li and Leyuan Fang and Meixiu Li and Benediktsson, Jon Atli
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 01/2015, Volume 53, Issue 1, pp. 144 - 153
This paper introduces a novel spectral-spatial classification method for hyperspectral images based on extended random walkers (ERWs), which consists of two... 
Support vector machines | Training | Image segmentation | Accuracy | Extended random walkers (ERWs) | optimization | hyperspectral image | Educational institutions | graph | spectral-spatial image classification | Hyperspectral imaging | Index terms - Extended random walkers (ERWs) | Graph | Spectral-spatial image classification | Hyperspectral image | Optimization | SUPPORT VECTOR MACHINES | PROFILES | FEATURE-EXTRACTION | REMOTE-SENSING IMAGES | MORPHOLOGICAL TRANSFORMATIONS | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | EMPIRICAL MODE DECOMPOSITION | SEGMENTATION | MARKOV RANDOM-FIELDS | Technology application | Usage | Graph theory | Schools | Artificial intelligence | Analysis | Classification | Statistical analysis | Samples | Mathematical models | Statistical methods | Pixels
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26. Full Text Skip-Connected Covariance Network for Remote Sensing Scene Classification
by He, Nanjun and Fang, Leyuan and Li, Shutao and Plaza, Javier and Plaza, Antonio
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 7/2019, pp. 1 - 14
This paper proposes a novel end-to-end learning model, called skip-connected covariance (SCCov) network, for remote sensing scene classification (RSSC). The... 
Learning systems | Training | Aggregates | Computational modeling | Neural networks | Feature extraction | scene recognition | Covariance pooling | multi-layer feature | Remote sensing | deep neural network
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27. Full Text An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising
by Shutao Li and Leyuan Fang and Haitao Yin
IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, 02/2012, Volume 59, Issue 2, pp. 417 - 427
In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D... 
Algorithm design and analysis | 3-D medical image denoising | Dictionaries | Correlation | k-means clustering | Prototypes | Clustering algorithms | Vectors | multiple-selection strategy | Dictionary learning | Biomedical imaging | sparse representation | PURSUIT | NOISE-REDUCTION | ENGINEERING, BIOMEDICAL | SPECKLE | Models, Theoretical | Artificial Intelligence | Humans | Image Interpretation, Computer-Assisted - methods | Imaging, Three-Dimensional - methods | Male | Tomography, X-Ray Computed | Pelvis - diagnostic imaging | Algorithms | Liver - diagnostic imaging | Ultrasonography | Head - diagnostic imaging | Female | Cluster Analysis | Measurement | Three-dimensional display systems | Usage | Random noise theory | Magnetic resonance imaging | Innovations | Diagnostic imaging | Mathematical optimization | Simulation methods | Pixels
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28. Full Text Guidelines for the use and interpretation of assays for monitoring autophagy
by Klionsky, Daniel J and Abdalla, Fabio C and Abeliovich, Hagai and Abraham, Robert T and Acevedo-Arozena, Abraham and Adeli, Khosrow and Agholme, Lotta and Agnello, Maria and Agostinis, Patrizia and Aguirre-Ghiso, Julio A and Ahn, Hyung Jun and Ait-Mohamed, Ouardia and Ait-Si-Ali, Slimane and Akematsu, Takahiko and Akira, Shizuo and Al-Younes, Hesham M and Al-Zeer, Munir A and Albert, Matthew L and Albin, Roger L and Alegre-Abarrategui, Javier and Aleo, Maria Francesca and Alirezaei, Mehrdad and Almasan, Alexandru and Almonte-Becerril, Maylin and Amano, Atsuo and Amaravadi, Ravi K and Amarnath, Shoba and Amer, Amal O and Andrieu-Abadie, Nathalie and Anantharam, Vellareddy and Ann, David K and Anoopkumar-Dukie, Shailendra and Aoki, Hiroshi and Apostolova, Nadezda and Arancia, Giuseppe and Aris, John P and Asanuma, Katsuhiko and Asare, Nana Y.O and Ashida, Hisashi and Askanas, Valerie and Askew, David S and Auberger, Patrick and Baba, Misuzu and Backues, Steven K and Baehrecke, Eric H and Bahr, Ben A and Bai, Xue-Yuan and Bailly, Yannick and Baiocchi, Robert and Baldini, Giulia and Balduini, Walter and Ballabio, Andrea and Bamber, Bruce A and Bampton, Edward T.W and Juhász, Gábor and Bartholomew, Clinton R and Bassham, Diane C and Bast, Robert C and Batoko, Henri and Bay, Boon-Huat and Beau, Isabelle and Béchet, Daniel M and Begley, Thomas J and Behl, Christian and Behrends, Christian and Bekri, Soumeya and Bellaire, Bryan and Bendall, Linda J and Benetti, Luca and Berliocchi, Laura and Bernardi, Henri and Bernassola, Francesca and Besteiro, Sébastien and Bhatia-Kissova, Ingrid and Bi, Xiaoning and Biard-Piechaczyk, Martine and Blum, Janice S and Boise, Lawrence H and Bonaldo, Paolo and Boone, David L and Bornhauser, Beat C and Bortoluci, Karina R and Bossis, Ioannis and Bost, Frédéric and Bourquin, Jean-Pierre and Boya, Patricia and Boyer-Guittaut, Michaël and Bozhkov, Peter V and Brady, Nathan R and Brancolini, Claudio and Brech, Andreas and Brenman, Jay E and Brennand, Ana and Bresnick, Emery H and Brest, Patrick and Bridges, Dave and Bristol, Molly L and Brookes, Paul S and Brown, Eric J and Brumell, John H and ... and Linköpings universitet and Institutionen för klinisk och experimentell medicin and Geriatrik and Institutionen för medicin och hälsa and Farmakologi and Experimentell patologi and Hälsouniversitetet and Cellbiologi
Autophagy, ISSN 1554-8627, 04/2012, Volume 8, Issue 4, pp. 445 - 544
In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and... 
stress | vacuole | autolysosome | flux | LC3 | autophagosome | lysosome | phagophore | Binding | Proteins | Landes | Calcium | Bioscience | Biology | Cell | Cycle | Cancer | Organogenesis | Phagophore | Lysosome | Autolysosome | Flux | Autophagosome | Vacuole | Stress | ACTIVATED PROTEIN-KINASE | CHAPERONE-MEDIATED AUTOPHAGY | VACUOLE TARGETING PATHWAY | ENDOPLASMIC-RETICULUM STRESS | ISOLATED RAT HEPATOCYTES | STARVATION-INDUCED AUTOPHAGY | GLUCAGON-INDUCED AUTOPHAGY | CELL BIOLOGY | PROGRAMMED CELL-DEATH | BREAST-CANCER CELLS | BETAINE HOMOCYSTEINE METHYLTRANSFERASE | Biological Assay - methods | Animals | Autophagy - genetics | Models, Biological | Humans | Life Sciences | Cellular Biology | Review | Medical and Health Sciences | Medicin och hälsovetenskap
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29. Full Text Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization
by Renwei Dian and Leyuan Fang and Shutao Li
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 07/2017, Volume 2017-, pp. 3862 - 3871
Hyperspectral image (HSI) super-resolution, which fuses a low-resolution (LR) HSI with a high-resolution (HR) multispectral image (MSI), has recently attracted... 
Tensile stress | Dictionaries | Encoding | Matrix decomposition | Sparse matrices | Spatial resolution
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30. Full Text Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification
by He, Nanjun and Paoletti, Mercedes E and Haut, Juan Mario and Fang, Leyuan and Li, Shutao and Plaza, Antonio and Plaza, Javier
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 02/2019, Volume 57, Issue 2, pp. 755 - 769
The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important... 
Training | Computational modeling | deep convolutional neural networks (CNNs) | Feature extraction | Data augmentation | Electronic mail | hyperspectral image (HIS) classification | Convolutional neural networks | multiscale covariance maps (MCMs) | Hyperspectral imaging | CNN | EXTINCTION PROFILES | IMPLEMENTATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | NEURAL-NETWORKS | Methodology | Spectral bands | Artificial neural networks | Spatial discrimination | Two dimensional models | Dimensions | Spectra | Exploitation | Tensors | Maps | Spatial data | Covariance | Neural networks | Classification | Multiscale methods | Image classification
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31. Full Text Hyperspectral Image Classification With Squeeze Multibias Network
by Fang, Leyuan and Liu, Guangyun and Li, Shutao and Ghamisi, Pedram and Benediktsson, Jon Atli
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 03/2019, Volume 57, Issue 3, pp. 1291 - 1301
A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical... 
Training | squeeze multibias network (SMBN) | Convolution | squeeze convolution module (SCM) | Roads | multibias module (MBM) | Feature extraction | Convolutional neural network (CNN) | Kernel | Hyperspectral imaging | hyperspectral image (HSI) classification | SUPERPIXEL | FUSION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | DEEP | Feature maps | Parameters | Modules | Artificial neural networks | Maps | Neural networks | Run time (computers) | Classification | Computer applications | Layers | Image classification
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32. Full Text Robust Object Tracking Based on Principal Component Analysis and Local Sparse Representation
by Haicang Liu and Shutao Li and Leyuan Fang
IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, 11/2015, Volume 64, Issue 11, pp. 2863 - 2875
Object tracking methods based on the principal component analysis (PCA) are effective against object change caused by illumination variation and motion blur.... 
sparse representation (SR) | Dictionaries | Computational modeling | similarity estimation | principal component analysis (PCA) | Robustness | Image sequences | particle filter | Object tracking | Principal component analysis | TARGET TRACKING | INSTRUMENTS & INSTRUMENTATION | VISUAL TRACKING | ENGINEERING, ELECTRICAL & ELECTRONIC | Robust statistics | Usage | Computer-generated environments | Computer simulation | Analysis | Pattern recognition | Object recognition (Computers) | Methods | Tracking | Similarity | Strategy | Mathematical models | Representations | Subspaces | Estimates
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33. Full Text Pixel-level image fusion: A survey of the state of the art
by Li, Shutao and Kang, Xudong and Fang, Leyuan and Hu, Jianwen and Yin, Haitao
Information Fusion, ISSN 1566-2535, 01/2017, Volume 33, pp. 100 - 112
Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine... 
Sparse representation | Medical imaging | Multiscale decomposition | Image fusion | Remote sensing | PERFORMANCE | WAVELET | RESOLUTION | ALGORITHM | QUALITY ASSESSMENT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | INFORMATION MEASURE | MUTUAL INFORMATION | FREQUENCY | MULTISCALE-DECOMPOSITION | TRANSFORM | COMPUTER SCIENCE, THEORY & METHODS
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