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Multimodal Behavior Analysis in the Wild: Advances and Challenges

Multimodal Behavior Analysis in the Wild: Advances and Challenges - Paperback

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Availability:In StockContributor:Xavier Alameda-Pineda, Elisa Ricci, Nicu SebeSeries:Computer Vision and Pattern RecognitionPublish date:11/16/2018Pages:498
Languages:EnglishPublisher:Academic PressISBN-13:9780128146019ISBN-10:012814601XUPC:9780128146019Book Category:Computers, Technology & EngineeringBook Subcategory:Artificial Intelligence, Automation, RoboticsBook Topic:Computer Vision & Pattern RecognitionSize:9.25 x 7.50 x 1.00 inchesWeight:0.849Product ID:SCNT4JDYH8

Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links.

This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing.

Languages:EnglishPublisher:Academic PressISBN-13:9780128146019ISBN-10:012814601XUPC:9780128146019Book Category:Computers, Technology & EngineeringBook Subcategory:Artificial Intelligence, Automation, RoboticsBook Topic:Computer Vision & Pattern RecognitionSize:9.25 x 7.50 x 1.00 inchesWeight:0.849Product ID:SCNT4JDYH8
Xavier Alameda-Pineda received his PhD from INRIA and University of Grenoble in2013. He was a post-doctoral researcher at CNRS/GIPSA-Lab and at the University of Trento, in the deep relational learning group. He is a research scientist at INRIA working on signal processing and machine learning for scene and behavior understanding using multimodal data. He is the winner of the best paper award of ACM MM 2015, the best student paper award at IEEE WASPAA 2015 and the best scientific paper award on image, speech, signal and video processing at IEEE ICPR 2016. He is member of IEEE and of ACM SIGMM.
Publisher: Academic Press

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