Search Results - "Advanced Methods and Deep Learning in Computer Vision"

Advanced Methods and Deep Learning in Computer Vision

Advanced Methods and Deep Learning in Computer Vision

Authors: Davies, E. R. Turk, Matthew

Index Terms: book

URL: https://univ.scholarvox.com/book/88930409

Advanced Methods and Deep Learning in Computer Vision

Authors: E. R. Davies Matthew Turk

Resource Type: eBook.

Subjects: Machine learning, Pattern recognition systems, Computer vision

Categories: TECHNOLOGY & ENGINEERING / Robotics, TECHNOLOGY & ENGINEERING / Automation

Dedication

Source: Advanced Methods and Deep Learning in Computer Vision ; page v ; ISBN 9780128221099

Availability: https://doi.org/10.1016/b978-0-12-822109-9.00004-7
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Contents

Source: Advanced Methods and Deep Learning in Computer Vision ; page vii-ix ; ISBN 9780128221099

Availability: https://doi.org/10.1016/b978-0-12-822109-9.00005-9
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Cluster Data Analysis for Optimization of Parameters of Object Detection Algorithms by Mobile Neural Network Models.

Authors: Arkhipov, P. O. Philippskikh, S. L. Tsukanov, M. V.

Source: Pattern Recognition & Image Analysis; Jun2025, Vol. 35 Issue 2, p219-224, 6p

Developing Compact Models Using Regression Confidence Forge Knowledge Distillation for IMU-Based Indoor Positioning System.

Authors: Putro, Nur Achmad Sulistyo Leu, Jenq-Shiou Ananto, Nias et al.

Source: IEEE Embedded Systems Letters; Apr2025, Vol. 17 Issue 2, p99-102, 4p

DEVELOPMENT OF A KAZAKH SIGN LANGUAGE RECOGNITION MODEL BASED ON YOLO-NAS.

Alternate Title: YOLO NAS НЕГІЗІНДЕ ҚАЗАҚ ЫМ ТІЛІН ТАНУ МОДЕЛІН ӘЗІРЛЕУ (Kazakh)
РАЗРАБОТКА МОДЕЛИ РАСПОЗНАВАНИЯ КАЗАХСКОГО ЯЗЫКА ЖЕСТОВ НА ОСНОВЕ YOLO NAS (Russian)

Authors: М., Othman D., Oralbekova U. G., Berzhanova

Source: Herald of the Kazakh-British Technical University; 2025, Vol. 22 Issue 1, p10-23, 14p

Subject Terms: CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, SIGN language, AUTHENTIC assessment, COMMUNICATION in education

Geographic Terms: KAZAKHSTAN

Corporate network anomaly detection methodology utilizing machine learning algorithms.

Authors: Baisholan, Nazerke Baisholanova, Karlygash Kubayev, Kazila et al.

Source: Smart Science; Dec2024, Vol. 12 Issue 4, p666-678, 13p

Subject Terms: COMPUTER network security, ANOMALY detection (Computer security), VALUE engineering, COMPUTER network traffic, SYSTEM analysis, INTRUSION detection systems (Computer security)

Classification of Medical Images by Deep Neural Networks.

Authors: Lvov, D. V. Khaibullin, A. N. Zagidullin, R. R. et al.

Source: Optics & Spectroscopy; Dec2024, Vol. 132 Issue 6-12, p497-500, 4p

Subject Terms: ARTIFICIAL neural networks, IMAGE recognition (Computer vision), DEEP learning, MEDICAL coding, DIAGNOSTIC imaging

ROV-Based Autonomous Maneuvering for Ship Hull Inspection with Coverage Monitoring.

Authors: Cardaillac, Alexandre Skjetne, Roger Ludvigsen, Martin

Source: Journal of Intelligent & Robotic Systems; Jun2024, Vol. 110 Issue 2, p1-24, 24p

An Approach to Pruning the Structure of Convolutional Neural Networks without Loss of Generalization Ability.

Authors: Chaoxiang Chen Kroshchanka, Aliaksandr Golovko, Vladimir et al.

Source: Pattern Recognition & Image Analysis; Jun2024, Vol. 34 Issue 2, p258-265, 8p

Review of Lightweight Deep Convolutional Neural Networks.

Authors: Chen, Fanghui Li, Shouliang Han, Jiale et al.

Source: Archives of Computational Methods in Engineering; May2024, Vol. 31 Issue 4, p1915-1937, 23p

Deep plug-and-play and deep unfolding methods for image restoration

Authors: Zhang, Kai Timofte, Radu Davies, E. Roy et al.

Contributors: Zhang, Kai Timofte, Radu Davies, E. Roy et al.

Source: Advanced Methods and Deep Learning in Computer Vision

Relation: http://hdl.handle.net/20.500.11850/593187

Availability: https://hdl.handle.net/20.500.11850/593187

Deep conditional image generation

Authors: Hua, Gang Chen, Dongdong

Source: Advanced Methods and Deep Learning in Computer Vision ; page 191-219 ; ISBN 9780128221099

Availability: https://doi.org/10.1016/b978-0-12-822109-9.00014-x
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Efficient methods for deep learning

Authors: Cai, Han Lin, Ji Han, Song

Source: Advanced Methods and Deep Learning in Computer Vision ; page 159-190

Availability: http://dx.doi.org/10.1016/b978-0-12-822109-9.00013-8
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Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems

Authors: Regazzoni, Carlo Krayani, Ali Slavic, Giulia et al.

Source: Advanced Methods and Deep Learning in Computer Vision ; page 449-479 ; ISBN 9780128221099

Availability: https://doi.org/10.1016/b978-0-12-822109-9.00022-9
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Learning for action-based scene understanding

Authors: Fermüller, Cornelia Maynord, Michael

Source: Advanced Methods and Deep Learning in Computer Vision ; page 373-403

Availability: http://dx.doi.org/10.1016/b978-0-12-822109-9.00020-5
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Deep face recognition using full and partial face images

Authors: Ugail, Hassan

Source: Advanced Methods and Deep Learning in Computer Vision ; page 221-241

Availability: http://dx.doi.org/10.1016/b978-0-12-822109-9.00015-1
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Long-term deep object tracking

Authors: Gavves, Efstratios Gupta, Deepak

Source: Advanced Methods and Deep Learning in Computer Vision ; page 337-371

Availability: http://dx.doi.org/10.1016/b978-0-12-822109-9.00019-9
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