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Gesture Recognition

  • Book
  • © 2017

Overview

Editors:
  1. Sergio Escalera

    1. University of Barcelona, Barcelona, Spain

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    You can also search for this editor in PubMed   Google Scholar

  2. Isabelle Guyon

    1. ChaLearn, Berkeley, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Vassilis Athitsos

    1. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Gives readers a comprehensive analysis on gesture recognition, defining a new taxonomy for the field
  • Focusses on supervised machine learning methods for gesture recognition
  • Presents an open-source C++ library for real-time gesture recognition
  • Reviews recent research involving deep learning architectures in order to deal with gesture and action recognition problems
  • Includes supplementary material: sn.pub/extras
  • Includes supplementary material: sn.pub/extras

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xii
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  2. Challenges in Multi-modal Gesture Recognition

    • Sergio Escalera, Vassilis Athitsos, Isabelle Guyon
    Pages 1-60

  3. Human Gesture Recognition on Product Manifolds

    • Yui Man Lui
    Pages 61-87

  4. Sign Language Recognition Using Sub-units

    • Helen Cooper, Eng-Jon Ong, Nicolas Pugeault, Richard Bowden
    Pages 89-118

  5. MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures with Low Probability of False Positives During Use

    • Daniel Kyu Hwa Kohlsdorf, Thad E. Starner
    Pages 119-154

  6. Language-Motivated Approaches to Action Recognition

    • Manavender R. Malgireddy, I. Nwogu, V. Govindaraju
    Pages 155-181

  7. A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives

    • Aleix M. Martinez, Shichuan Du
    Pages 183-202

  8. Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs

    • Sunita Nayak, Kester Duncan, Sudeep Sarkar, Barbara Loeding
    Pages 203-230

  9. Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos

    • Anastasios Roussos, Stavros Theodorakis, Vassilis Pitsikalis, Petros Maragos
    Pages 231-271

  10. Discriminative Hierarchical Part-Based Models for Human Parsing and Action Recognition

    • Yang Wang, Duan Tran, Zicheng Liao, David Forsyth
    Pages 273-301

  11. Keep It Simple and Sparse: Real-Time Action Recognition

    • Sean Ryan Fanello, Ilaria Gori, Giorgio Metta, Francesca Odone
    Pages 303-328

  12. One-Shot Learning Gesture Recognition from RGB-D Data Using Bag of Features

    • Jun Wan, Qiuqi Ruan, Wei Li, Shuang Deng
    Pages 329-364

  13. One-Shot-Learning Gesture Recognition Using HOG-HOF Features

    • Jakub Konečný, Michal Hagara
    Pages 365-385

  14. Multi-layered Gesture Recognition with Kinect

    • Feng Jiang, Shengping Zhang, Shen Wu, Yang Gao, Debin Zhao
    Pages 387-416

  15. Bayesian Co-Boosting for Multi-modal Gesture Recognition

    • Jiaxiang Wu, Jian Cheng
    Pages 417-441

  16. Transfer Learning Decision Forests for Gesture Recognition

    • Norberto A. Goussies, Sebastián Ubalde, Marta Mejail
    Pages 443-466

  17. Multimodal Gesture Recognition via Multiple Hypotheses Rescoring

    • Vassilis Pitsikalis, Athanasios Katsamanis, Stavros Theodorakis, Petros Maragos
    Pages 467-496

  18. The Gesture Recognition Toolkit

    • Nicholas Gillian, Joseph A. Paradiso
    Pages 497-502

  19. Robust Online Gesture Recognition with Crowdsourced Annotations

    • Long-Van Nguyen-Dinh, Alberto Calatroni, Gerhard Tröster
    Pages 503-537

  20. Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

    • Maryam Asadi-Aghbolaghi, Albert Clapés, Marco Bellantonio, Hugo Jair Escalante, Víctor Ponce-López, Xavier Baró et al.
    Pages 539-578
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Keywords

About this book

This book presents a selection of chapters, written by leading international researchers, related to the automatic analysis of gestures from still images and multi-modal RGB-Depth image sequences. It offers a comprehensive review of vision-based approaches for supervised gesture recognition methods that have been validated by various challenges. Several aspects of gesture recognition are reviewed, including data acquisition from different sources, feature extraction, learning, and recognition of gestures.

Editors and Affiliations

  • University of Barcelona, Barcelona, Spain

    Sergio Escalera

  • ChaLearn, Berkeley, USA

    Isabelle Guyon

  • Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA

    Vassilis Athitsos

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