rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Gesture Recognition
    sub title: Theory and Applications
  name:
    name part: Miao, Qiguang.
    role:
      role term: creator
  name:
    name part: Li, Yunan.
  name:
    name part: Liu, Xiangzeng.
  name:
    name part: Liu, Ruyi.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (225 p.)
  abstract: 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.
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  note: Qiguang Miao, Yunan Li, Xiangzeng Liu, Ruyi Liu.
  note: 4.1.1.2 Weight sharing
  note: Includes bibliographical references and index.
  subject:
    topic: Gesture recognition (Computer science)
  subject:
    topic: Human-computer interaction
  classification: TA1652 .M53 2024
  classification: QA76.9.H85
  classification: 006.42
  classification: 004.01/9
  related item:
    title info:
      title: Gesture Recognition
    name:
      name part: Miao, Qiguang
    origin info:
      publisher: San Diego : Elsevier,c2024
  identifier: 0443289603
  identifier: 9780443289606
  identifier: 044328959X
  identifier: 9780443289590
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: EBLCP
    record creation date: 240727
    record change date: 20260327180120.5
    record identifier: in00005773386
    language of cataloging:
      language term: eng