http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Gesture Recognition: Theory and Applications covers this important topic in computer science and language technology that has a goal of interpreting human gestures via mathematical algorithms. The book begins by examining the computer vision-based gesture recognition method, focusing on the theory and related research results of various recent gesture recognition technologies. The book takes the evolutions of gesture recognition technology as a clue, systematically introducing gesture recognition methods based on handcrafted features, convolutional neural networks, recurrent neural networks, multimodal data fusion, and visual attention mechanisms. Three gesture recognition-based HCI (Human Computer Interaction) practical cases are introduced. Finally, the book looks at emerging research trends and application.
Front Cover -- Gesture Recognition -- Copyright Page -- Contents -- 1 Basic concepts and development of gesture recognition -- 1.1 Principles of gesture recognition -- 1.1.1 Gesture in human society -- 1.1.2 Gesture and human-computer interaction -- 1.2 Development of gesture recognition algorithms -- 1.2.1 Methods based on handicraft features -- 1.2.2 Methods based on probabilistic graphical model -- 1.2.3 Methods based on bag of visual words -- 1.2.4 Methods based on neural network -- 1.3 Current challenges in the field of gesture recognition -- 1.4 Summary -- References
2 Common datasets in the field of gesture recognition -- 2.1 Image-based gesture dataset -- 2.1.1 American Sign Language fingerspelling dataset -- 2.1.2 MU HandImages ASL gesture dataset -- 2.1.3 LaRED gesture dataset -- 2.1.4 Marcel gesture dataset -- 2.1.5 Senz3D gesture dataset -- 2.2 Video-based gesture dataset -- 2.2.1 20BN-jester dataset -- 2.2.2 RWTH-PHOENIX-weather dataset -- 2.2.3 CSL series datasets -- 2.2.4 DEVISIGN sign language dataset -- 2.2.5 CGD series datasets -- 2.2.5.1 CGD 2011 -- 2.2.5.2 CGD 2013 -- 2.2.5.3 CGD 2016 -- 2.2.6 Traffic police gesture dataset -- 2.2.7 SKIG dataset
2.2.8 EgoGesture dataset -- 2.2.9 MSRC-12 dataset -- 2.2.10 NvGesture dataset -- 2.3 Dataset summary -- 2.4 Summary -- References -- 3 Gesture recognition method based on handicraft features -- 3.1 Hand region segmentation -- 3.1.1 Edge information-based segmentation methods -- 3.1.1.1 Edge operator-based segmentation approach -- 3.1.1.2 Active contour model-based segmentation -- 3.1.2 Motion analysis-based segmentation methods -- 3.1.2.1 Background subtraction-based segmentation -- 3.1.2.2 Interframe difference threshold-based method -- 3.1.2.3 Optical flow field-driven segmentation
3.1.3 Skin color feature-based segmentation methods -- 3.1.4 Summary -- 3.2 Gesture feature extraction -- 3.2.1 Haar-like features -- 3.2.2 Local binary pattern features -- 3.2.3 SIFT features -- 3.2.3.1 Build Gaussian difference pyramid -- 3.2.3.2 Determine location of keypoints -- 3.2.3.2.1 Primary selection of keypoints -- 3.2.3.2.2 Adjustment of keypoints -- 3.2.3.2.3 Removal of edge effects -- 3.2.3.3 Direction of keypoints -- 3.2.3.4 Construction of keypoint descriptors -- 3.2.4 SURF features -- 3.2.4.1 Construction of scale space -- 3.2.4.2 Location of keypoints
3.2.4.3 Determination of the direction of feature points -- 3.2.4.4 Generation of feature descriptors -- 3.2.5 Features of histogram of oriented gradient -- 3.2.6 Features of histogram of oriented optical flow -- 3.2.7 Summary -- 3.3 Gesture recognition -- 3.3.1 Template matching -- 3.3.2 Finite-state machine -- 3.3.3 Dynamic time warping -- 3.4 Summary -- References -- 4 Gesture recognition method based on convolutional neural network -- 4.1 Basic operations of deep convolutional neural network -- 4.1.1 Characteristics of convolutional neural network -- 4.1.1.1 Local connection
Qiguang Miao, Yunan Li, Xiangzeng Liu, Ruyi Liu.
Includes bibliographical references and index.