Hand Shape and Gesture Recognition using Multiscale Template Matching, Background Subtraction and Binary Image Analysis

Fuente: arXiv
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1. Verfasser: Saichandran, Ketan Suhaas
Format: Preprint
Veröffentlicht: 2024
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author Saichandran, Ketan Suhaas
author_facet Saichandran, Ketan Suhaas
contents This paper presents a hand shape classification approach employing multiscale template matching. The integration of background subtraction is utilized to derive a binary image of the hand object, enabling the extraction of key features such as centroid and bounding box. The methodology, while simple, demonstrates effectiveness in basic hand shape classification tasks, laying the foundation for potential applications in straightforward human-computer interaction scenarios. Experimental results highlight the system's capability in controlled environments.
format Preprint
id arxiv_https___arxiv_org_abs_2402_09663
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Hand Shape and Gesture Recognition using Multiscale Template Matching, Background Subtraction and Binary Image Analysis
Saichandran, Ketan Suhaas
Computer Vision and Pattern Recognition
This paper presents a hand shape classification approach employing multiscale template matching. The integration of background subtraction is utilized to derive a binary image of the hand object, enabling the extraction of key features such as centroid and bounding box. The methodology, while simple, demonstrates effectiveness in basic hand shape classification tasks, laying the foundation for potential applications in straightforward human-computer interaction scenarios. Experimental results highlight the system's capability in controlled environments.
title Hand Shape and Gesture Recognition using Multiscale Template Matching, Background Subtraction and Binary Image Analysis
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2402.09663