Quantitative comparison of motion history image variants for video-based depression assessment

Quantitative comparison of motion history image variants for video-based depression assessment

Abstract Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Mo...

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Bibliographic Details
Main Authors: Anastasia Pampouchidou, Matthew Pediaditis, Anna Maridaki, Muhammad Awais, Calliope-Marina Vazakopoulou, Stelios Sfakianakis, Manolis Tsiknakis, Panagiotis Simos, Kostas Marias, Fan Yang, Fabrice Meriaudeau
Format: Article
Language:English
Published: SpringerOpen 2017-09-01
Series:EURASIP Journal on Image and Video Processing
Subjects:
Depression assessment
Affective computing
Machine learning
Image processing
Facial image analysis
Motion history image
Online Access:http://link.springer.com/article/10.1186/s13640-017-0212-3

Internet

http://link.springer.com/article/10.1186/s13640-017-0212-3

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