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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System.

Fahmid Al Farid¹, Noramiza Hashim¹, Junaidi Abdullah¹

¹Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.

Journal of Imaging

|June 23, 2022

Summary

This summary is machine-generated.

This review analyzes vision-based hand gesture recognition challenges and advancements from 2012-2022. It identifies limitations in image acquisition, segmentation, feature extraction, and classification, highlighting areas for future research in real-time gesture recognition systems.

Keywords:

deep learning feature extraction gesture classification gesture recognition recognition accuracy

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Area of Science:

Computer Vision
Human-Computer Interaction
Artificial Intelligence

Background:

Vision-based hand gesture recognition is crucial for intuitive human-computer interaction.
Real-time implementation faces challenges in image acquisition, segmentation, tracking, feature extraction, and classification.
Existing research exhibits significant variability in recognition accuracy, ranging from 68% to 97%.

Purpose of the Study:

To systematically review and analyze vision-based hand gesture recognition research from 2012 to 2022.
To identify limitations and areas for improvement across the entire gesture recognition pipeline.
To provide a categorized resource of notable research and methodologies in the field.

Main Methods:

Conducted a literature review using specific keywords across major online databases, identifying 108 relevant articles.
Categorized and summarized methodologies from selected research works.
Analyzed recognition accuracy and identified common limitations.

Main Results:

Identified key challenges in image acquisition, segmentation, feature extraction, and classification stages.
Documented a wide range of recognition accuracies, averaging 86.6%.
Highlighted limitations including ambiguous gesture interpretations and complex hand articulations.

Conclusions:

Significant progress has been made, but real-time vision-based hand gesture recognition remains challenging.
Further research is needed to address limitations in handling diverse interpretations and non-rigid hand dynamics.
This review offers a comprehensive overview and categorization of current techniques, guiding future development.