Gesture-Based Object Recognition using Histograms of Guiding Strokes

Abstract

Humans perform iconic gestures to refer to entities through embodying their shapes. For instance, people often gesture the outline of an object (e.g. a circle for a ball) when referring to it during communication. In this paper, we present a gesture-based object recognition algorithm that enables natural human-computer interaction involving iconic gestures. Based on our analysis of multiple gesture performances, we propose a new 3D motion description of iconic gestures, called Histograms of Guiding Strokes (HoGS), which successfully summarizes hand dynamic during gestures. Our gesture-based object recognition algorithm compares favorably to human judgment performance and outperforms most conventional gesture recognition approaches.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
AD1170983

Entities

People

  • Amir Sadeghipour
  • Louis-Philippe Morency
  • Stefan Kopp

Organizations

  • Bielefeld University
  • University of Southern California

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • Cognitive Science
  • Cognitive Systems Engineering
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Data Analysis
  • Human-Computer Interaction
  • Human-Computer Interfaces
  • Human-Machine Interaction
  • Machine Learning
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Supervised Machine Learning
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.
  • Systems Analysis and Design