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davies e. r. (curatore); turk matthew (curatore) - advanced methods and deep learning in computer vision

Advanced Methods and Deep Learning in Computer Vision


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Lingua: Inglese
Pubblicazione: 11/2021

Note Editore

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.

This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.

  • Provides an important reference on deep learning and advanced computer methods that was created by leaders in the field
  • Illustrates principles with modern, real-world applications
  • Suitable for self-learning or as a text for graduate courses


Preface Emlyn Roy Davies, Octavia Camps and Matthew Turk 1. The changing face of computer vision Emlyn Roy Davies 2. Developments in machine learning: from deep networks to deep functional scene understanding Cornelia Fermüller 3. Adversarial examples in deep learning Andrea Cavallaro 4. Learning with reinforcement and limited supervision Amit Roy Chowdhury 5. Self-supervised event segmentation Sudeep Sarkar 6. Advanced methods for robust object detection Nuno Vasconcelos 7. Recognition and tracking in scenes containing multiple moving objects Michael Felsberg 8. Domain adaptation and incremental learning for semantic segmentation Pietro Zanuttigh 9. Methodologies for partial and complete face detection Hassan Ugail 10. Modern approaches to anomaly detection Carlo Regazzoni 11. Learning and reconstructing complex 3D objects from multiple views Gerard Pons-Moll 12. Dynamics-based invariants for video analytics Octavia Camps 13. State of the art on object re-identification Bastian Leibe 14. Principled methods for improving efficiency of deep neural networks Song Han 15. Methodology for long-term visual object tracking Efstratios Gavves 16. Conditional image generation for learning the structure of visual objects Gang Hua 17. Domain adaptive deep learning Rama Chellappa 18. Combining model-based and deep learned-based approaches for image restoration Radu Timofte


Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection and neural networks. He has published more than 200 papers, and three books. Machine Vision: Theory, Algorithms, Practicalities (1990) has been widely used internationally for more than 25 years, and is now out in this much enhanced fifth edition. Roy holds a DSc at the University of London, and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition.
Matthew Turk is a professor and department chair of the Department of Computer Science at the University of California, Santa Barbara, California. He was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013[1] for his contributions to computer vision and perceptual interfaces. Starting on July 1st, he will be the president of the Toyota Technological Institute at Chicago[2]. In 2014, Turk was named a Fellow of the International Association for Pattern Recognition (IAPR)[3] for his contributions to computer vision and vision based interaction.

Altre Informazioni



Condizione: Nuovo
Collana: Computer Vision and Pattern Recognition
Dimensioni: 235 x 191 mm
Formato: Brossura
Illustration Notes:Approx. 175 illustrations (125 in full color)
Pagine Arabe: 582

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