Qiang Ji

Gang Hua

Kamal Nasrollahi

Thomas B. Moeslund

This book constitutes the thoroughly refereed conference proceedings of the International Workshop on Face and Facial Expression Recognition from Real World Videos in conjunction with the 22nd International Conference on Pattern Recognition held in Stockholm, Sweden, in August 2014. The 11 revised full papers were carefully reviewed and selected from numerous submissions and cover topics such as Face Recognition, Face Alignment, Facial Expression Recognition and Facial Images.

International Workshop, Stockholm, Sweden, August 24, 2014, Revised Selected Papers

Face and Facial Expression Recognition from Real World Videos

Phoumin Han

Farhad Taghizadeh-Hesary

Xunpeng (Roc) Shi

Dayong Zhang

Energy Market and Energy Transition: Dynamics and Prospects

Xiang Bai

Shiguang Shan

Shuicheng Yan

Yi Fang

Yangqing Jia

Meina Kan

Chunhua Shen

Jingdong Wang

Gui-Song Xia

Zhaoxiang Zhang

This book constitutes the proceedings of the Third Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2018, and the Second International Workshop on Deep Learning for Pattern Recognition, DLPR 2018, held at the 24th International Conference on Pattern Recognition, ICPR 2018, in Beijing, China, in August 2018. The 7 papers presented in this volume were carefully reviewed and selected from 9 submissions. They deal with topics such as histopathological images, action recognition, scene text detection, speech recognition, object classification, presentation attack detection, and driver drowsiness detection.

Third International Workshop, FFER 2018, and Second International Workshop, DLPR 2018, Beijing, China, August 20, 2018, Revised Selected Papers

Video Analytics. Face and Facial Expression Recognition

Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. Discusses PGM theories and techniques with computer vision examples Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision Includes an extensive list of references, online resources and a list of publicly available and commercial software Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction

Probabilistic Graphical Models for Computer Vision

Qiang Ji

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