Gesture Recognition : Theory and Applications /
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 a...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA :
Elsevier,
[2024]
|
| Schlagworte: | |
| Online-Zugang: | ScienceDirect |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000cam a22000007i 4500 | ||
|---|---|---|---|
| 001 | on1446130887 | ||
| 003 | OCoLC | ||
| 005 | 20260915095237.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 240727s2024 ne ob 001 0 eng d | ||
| 040 | |a EBLCP |b eng |e rda |c EBLCP |d YDX |d OCLCQ |d OCLCO |d N$T |d OCLCO |d YDX |d OPELS |d UKAHL |d OCLCL |d OCLCF |d OCLCO | ||
| 019 | |a 1445896054 |a 1453442067 | ||
| 020 | |a 0443289603 |q electronic book | ||
| 020 | |a 9780443289606 |q electronic book | ||
| 020 | |z 044328959X | ||
| 020 | |z 9780443289590 | ||
| 035 | |a (OCoLC)1446130887 |z (OCoLC)1445896054 |z (OCoLC)1453442067 | ||
| 050 | 4 | |a TA1652 |b .M53 2024 | |
| 050 | 4 | |a QA76.9.H85 | |
| 082 | 0 | 4 | |a 006.42 |2 23/eng/20240927 |1 https://id.oclc.org/worldcat/ddc/E3gG6pTKRXhfYkpV6CXrjFbFGc |
| 082 | 0 | 4 | |a 004.01/9 |2 23/eng/20240828 |1 https://id.oclc.org/worldcat/ddc/E3chcc44c76JP4r83tyJpx8Jvm |
| 100 | 1 | |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. |
| 264 | 1 | |a Amsterdam, Netherlands ; |a Oxford, United Kingdom ; |a Cambridge MA : |b Elsevier, |c [2024] | |
| 300 | |a 1 online resource (225 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 500 | |a 4.1.1.2 Weight sharing | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on September 27, 2024). | ||
| 520 | |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. | ||
| 650 | 0 | |a Gesture recognition (Computer science) | |
| 650 | 0 | |a Human-computer interaction. | |
| 650 | 7 | |a Gesture recognition (Computer science) |2 fast |0 (OCoLC)fst02033895 | |
| 650 | 7 | |a Human-computer interaction |2 fast |0 (OCoLC)fst00963494 | |
| 700 | 1 | |a Li, Yunan. | |
| 700 | 1 | |a Liu, Xiangzeng. | |
| 700 | 1 | |a Liu, Ruyi. | |
| 776 | 0 | 8 | |i Print version: |a Miao, Qiguang |t Gesture Recognition |d San Diego : Elsevier,c2024 |z 9780443289590 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443289590 |