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|a TXAM
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|a Miao, Qiguang.
|1 https://id.oclc.org/worldcat/entity/E39PCjrVvMt9XQFgjxhdGYQqFC
|
| 245 |
1 |
0 |
|a Gesture Recognition :
|b Theory and Applications /
|c Qiguang Miao, Yunan Li, Xiangzeng Liu, Ruyi Liu.
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| 264 |
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1 |
|a Amsterdam, Netherlands ;
|a Oxford, United Kingdom ;
|a Cambridge MA :
|b Elsevier,
|c [2024]
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| 300 |
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|a 1 online resource (225 p.)
|
| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a Includes bibliographical references and index.
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| 505 |
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|a 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
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| 505 |
8 |
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|a 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
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| 505 |
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|a 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
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| 505 |
8 |
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|a 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
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| 505 |
8 |
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|a 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
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| 500 |
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|a 4.1.1.2 Weight sharing
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| 588 |
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|a Description based on online resource; title from digital title page (viewed on September 27, 2024).
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| 520 |
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|a 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.
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| 650 |
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0 |
|a Gesture recognition (Computer science)
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| 650 |
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0 |
|a Human-computer interaction.
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| 655 |
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7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
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|a Li, Yunan.
|
| 700 |
1 |
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|a Liu, Xiangzeng.
|
| 700 |
1 |
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|a Liu, Ruyi.
|
| 710 |
2 |
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|a ScienceDirect (Online service)
|
| 776 |
0 |
8 |
|i Print version:
|a Miao, Qiguang
|t Gesture Recognition
|d San Diego : Elsevier,c2024
|z 9780443289590
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|z Connect to the full text of this electronic book
|t 0
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|a Elsevier ScienceDirect 2026-2027
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|a Texas A&M University
|b College Station
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|h Library of Congress classification
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