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Gesture Recognition

Overview of attention for book
Cover of 'Gesture Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Challenges in Multi-modal Gesture Recognition
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    Chapter 2 Human Gesture Recognition on Product Manifolds
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    Chapter 3 Sign Language Recognition Using Sub-units
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    Chapter 4 MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures with Low Probability of False Positives During Use
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    Chapter 5 Language-Motivated Approaches to Action Recognition
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    Chapter 6 A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives
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    Chapter 7 Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs
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    Chapter 8 Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos
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    Chapter 9 Discriminative Hierarchical Part-Based Models for Human Parsing and Action Recognition
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    Chapter 10 Keep It Simple and Sparse: Real-Time Action Recognition
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    Chapter 11 One-Shot Learning Gesture Recognition from RGB-D Data Using Bag of Features
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    Chapter 12 One-Shot-Learning Gesture Recognition Using HOG-HOF Features
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    Chapter 13 Multi-layered Gesture Recognition with Kinect
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    Chapter 14 Bayesian Co-Boosting for Multi-modal Gesture Recognition
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    Chapter 15 Transfer Learning Decision Forests for Gesture Recognition
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    Chapter 16 Multimodal Gesture Recognition via Multiple Hypotheses Rescoring
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    Chapter 17 The Gesture Recognition Toolkit
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    Chapter 18 Robust Online Gesture Recognition with Crowdsourced Annotations
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    Chapter 19 Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey
Attention for Chapter 12: One-Shot-Learning Gesture Recognition Using HOG-HOF Features
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Chapter title
One-Shot-Learning Gesture Recognition Using HOG-HOF Features
Chapter number 12
Book title
Gesture Recognition
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-57021-1_12
Book ISBNs
978-3-31-957020-4, 978-3-31-957021-1
Authors

Jakub Konečný, Michal Hagara, Konečný, Jakub, Hagara, Michal

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 1%
Netherlands 1 1%
Indonesia 1 1%
Ireland 1 1%
Canada 1 1%
China 1 1%
United States 1 1%
Unknown 78 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 36%
Student > Master 14 16%
Researcher 8 9%
Student > Bachelor 7 8%
Professor > Associate Professor 5 6%
Other 11 13%
Unknown 9 11%
Readers by discipline Count As %
Computer Science 47 55%
Engineering 23 27%
Biochemistry, Genetics and Molecular Biology 1 1%
Social Sciences 1 1%
Psychology 1 1%
Other 2 2%
Unknown 10 12%