Multimodal Behavior Analysis in the Wild Advances and Challenges
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Sprache:Englisch
155,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
13.11.2018
Herausgeber
Xavier Alameda-Pineda + weitereVerlag
Elsevier Science & Techn.Seitenzahl
498 (Printausgabe)
Sprache
Englisch
EAN
9780128146026
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.
- Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios
- Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources
- Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
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.
- Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios
- Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources
- Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
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