Suchergebnisse - multi-layered convolutional sparse coding~

Analysis of Top-Down Connections in Multi-Layered Convolutional Sparse Coding

Autoren: Joakim Edlund Christine Guillemot Marten Sjostrom

Quelle: 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP). :1-6

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A Multilayered Convolutional Sparse Coding Framework for Modeling of Pooling Operation of Convolution Neural Networks

Autoren: Jawad Ali Shah Abdul Wahid Adnan Umar Khan et al.

Quelle: 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). :1-5

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9057334
http://xplorestaging.ieee.org/ielx7/9049928/9057290/09057334.pdf?arnumber=9057334

Analysis of Top-Down Connections in Multi-Layered Convolutional Sparse Coding

Autoren: Edlund, Joakim Guillemot, Christine Sjöström, Mårten

Index Begriffe: Multi-Layered Convolutional Sparse Coding, Predictive Coding, Signal Processing, Signalbehandling, Computer graphics and computer vision, Datorgrafik och datorseende, Conference paper, info:eu-repo/semantics/conferenceObject, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43563
2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

Multi-Layered Basis Pursuit Algorithms for Classification of MR Images of Knee ACL Tear

Autoren: Abdul Wahid Jawad Ali Shah Adnan Umar Khan et al.

Quelle: IEEE Access, Vol 8, Pp 205424-205435 (2020)

Schlagwörter: Artificial intelligence, multi-layer convolutional sparse coding, Biomedical Engineering, Convolutional neural network, Projections and Outcomes of Arthroplasty Surgery in the US, 02 engineering and technology, FOS: Medical engineering, Pattern recognition (psychology), Biomechanical Analysis, iterative shrinkage algorithms, Engineering, Health Sciences, Machine learning, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Basis pursuit, knee MR image classification, Deep learning, Advances in Photoacoustic Imaging and Tomography, Computer science, Contextual image classification, TK1-9971, Algorithm, Knee Biomechanics, In Vivo Imaging, Physical Sciences, Medicine, Surgery, Electrical engineering. Electronics. Nuclear engineering, Anterior Cruciate Ligament Injuries in Athletes

Zugangs-URL: https://doaj.org/article/091ac545b21d4301a35bc141ed52c44a
https://dblp.uni-trier.de/db/journals/access/access8.html#WahidSKUA20
https://doi.org/10.1109/ACCESS.2020.3037745
https://ieeexplore.ieee.org/document/9257450/

Multi Attention Convolutional Sparse Coding U-Net for Enhanced Land-Use and Land-Cover Segmentation Using Hyperspectral Images.

Autoren: Yele, Vijaykumar P. Badhe, Namdeo Baban Alegavi, Sujata et al.

Quelle: Sensing & Imaging; 5/20/2025, Vol. 26 Issue 1, p1-32, 32p

A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks

Autoren: Abdul Wahid Khan, Adnan Umar Mukhtarullah et al.

Schlagwörter: T Technology (General)

Dateibeschreibung: application/pdf

Relation: http://irep.iium.edu.my/80454/1/80454%20A%20Multilayered%20Convolutional%20Sparse%20Coding%20Framework.pdf; http://irep.iium.edu.my/80454/2/80454%20A%20Multilayered%20Convolutional%20Sparse%20Coding%20Framework%20SCOPUS.pdf; Abdul Wahid and Khan, Adnan Umar and , Mukhtarullah and Khan, Sheroz and Shah, Jawad (2019) A multilayered convolutional sparse coding framework for modeling of pooling operation of convolution neural networks. In: 2019 IEEE 6th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA 2019), 27 - 29 Aug 2019, Kuala Lumpur, Malaysia.

Verfügbarkeit: http://irep.iium.edu.my/80454/
http://irep.iium.edu.my/80454/1/80454%20A%20Multilayered%20Convolutional%20Sparse%20Coding%20Framework.pdf
http://irep.iium.edu.my/80454/2/80454%20A%20Multilayered%20Convolutional%20Sparse%20Coding%20Framework%20SCOPUS.pdf
https://ieeexplore.ieee.org/document/9057334

Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Autoren: Abdul Wahid Jawad Ali Shah Adnan Umar Khan et al.

Quelle: IEEE Access, Vol 8, Pp 186222-186232 (2020)

Schlagwörter: multi-layered convolutional sparse coding, Signal processing, Radiology, Nuclear Medicine and Imaging, Artificial intelligence, Economics, Computational Mechanics, Biomedical Engineering, Convolutional neural network, 02 engineering and technology, FOS: Medical engineering, Pattern recognition (psychology), Sparse Approximation, 03 medical and health sciences, Engineering, Magnetic resonance imaging, 0302 clinical medicine, Compressed Sensing, Health Sciences, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Orthogonal Matching Pursuit, Basis pursuit, Economic growth, Signal reconstruction, Radar, Deep learning, Advances in Photoacoustic Imaging and Tomography, Compressive sensing, Theory and Applications of Compressed Sensing, iterative thresholding algorithms, Magnetic Resonance Imaging, Computer science, TK1-9971, Iterative reconstruction, inverse problems in imaging, Thresholding, Physical Sciences, Convergence (economics), Matching pursuit, Telecommunications, Medicine, Quantitative Susceptibility Mapping, Compressed sensing, Computer vision, Electrical engineering. Electronics. Nuclear engineering, Radiology, Magnetic Resonance Imaging Applications in Medicine

Zugangs-URL: https://doaj.org/article/0f99a9b6256d4122baa3bc01a9e6bffa
https://dblp.uni-trier.de/db/journals/access/access8.html#WahidSKAR20
https://ieeexplore.ieee.org/document/9214501

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization.

Autoren: Zhang, Han Lin, Jing Hua, Jiadong et al.

Quelle: Structural Health Monitoring; Jul2022, Vol. 21 Issue 4, p1790-1804, 15p

Schlagwörter: LAMB waves, DEEP learning, STRUCTURAL health monitoring, FEATURE extraction, NONDESTRUCTIVE testing, CONVOLUTIONAL neural networks

CMCS-net: image compressed sensing with convolutional measurement via DCNN.

Autoren: Xie, Yahong Wang, Hailin Wang, Jianjun

Quelle: IET Image Processing (Wiley-Blackwell). 2020, Vol. 14 Issue 15, p3839-3850. 12p.

Schlagwörter: *DEEP learning, *CONVOLUTIONAL neural networks, *PROBLEM solving, *COMPRESSED sensing, *SIGNAL convolution, *COMPUTATIONAL complexity, *DATA compression

Denoising convolutional neural network inspired via multi-layer convolutional sparse coding.

Autoren: Wen, Zejia Wang, Hailin Gong, Yingfan et al.

Quelle: Journal of Electronic Imaging; Mar/Apr2021, Vol. 30 Issue 2, p23007-23007, 1p

Schlagwörter: CONVOLUTIONAL neural networks, RECURRENT neural networks, COMPUTER vision, THRESHOLDING algorithms, IMAGE denoising

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Autoren: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Quelle: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

Improved Image Generation via Sparse Modeling

Autoren: Ganz, Roy Elad, Michael

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/2104.00464

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception.

Autoren: Brucklacher, Matthias Bohté, Sander M. Mejias, Jorge F. et al.

Quelle: Frontiers in Computational Neuroscience; 2023, p1-15, 15p

Schlagwörter: VISUAL perception, LINEAR network coding, IMAGE representation, MACAQUES, FORECASTING, FUSIFORM gyrus

People search via deep compressed sensing techniques.

Autoren: Lu, Bing-Xian Tsai, Yu-Chung Tseng, Kuo-Shih

Quelle: Robotica; Jul2022, Vol. 40 Issue 7, p2320-2348, 29p

Schlagwörter: COMPRESSED sensing, SUBMODULAR functions, DRONE aircraft, SEARCH algorithms

Early prediction of Alzheimer's disease using convolutional neural network: a review.

Autoren: Patil, Vijeeta Madgi, Manohar Kiran, Ajmeera

Quelle: Egyptian Journal of Neurology, Psychiatry & Neurosurgery; 11/17/2022, Vol. 58, p1-10, 10p

Schlagwörter: ALZHEIMER'S disease, CONVOLUTIONAL neural networks, MACHINE learning, ARTIFICIAL intelligence, APOLIPOPROTEIN E4, NEUROBEHAVIORAL disorders

MLK-SVD, the new approach in deep dictionary learning.

Autoren: Montazeri, Azadeh Shamsi, Mahboubeh Dianat, Rouhollah

Quelle: Visual Computer; Apr2021, Vol. 37 Issue 4, p707-715, 9p

Schlagwörter: MACHINE learning, IMAGE representation, DEEP learning

Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition.

Autoren: Shicao Luo Yongsheng Ding Kuangrong Hao

Quelle: 2015 Chinese Automation Congress (CAC); 2015, p565-570, 6p

ENCODER, DECODER AND METHODS FOR CODING A PICTURE USING A CONVOLUTIONAL NEURAL NETWORK

Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences.

Autoren: Baker, Janet M. Cariani, Peter

Quelle: Frontiers in Computational Neuroscience; 2025, p1-35, 35p

Schlagwörter: TELECOMMUNICATION systems, ASSOCIATIVE storage, DELAY lines, COMMUNICATION models, TEMPORARY stores, MOTOR learning

3D shape classification with NNLS coding and optimal projections technique.

Autoren: Fotopoulou, F. Oikonomou, S. Economou, G.

Quelle: Multimedia Tools & Applications; Dec2019, Vol. 78 Issue 24, p34689-34706, 18p

Schlagwörter: EIGENANALYSIS, SUPPORT vector machines, LINEAR operators, GEOMETRIC shapes, FEATURE extraction, CLASSIFICATION