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Comparing shape descriptor methods for different color space and lighting conditions

Published online by Cambridge University Press:  04 December 2019

Abhishek Mukhopadhyay Open the ORCID record for Abhishek Mukhopadhyay [Opens in a new window] ,
Imon Mukherjee  and
Pradipta Biswas
Abhishek Mukhopadhyay*
Affiliation:
Indian Institute of Science, Centre for Product Design and Manufacturing, Bengaluru, India
Imon Mukherjee
Affiliation:
Indian Institute of Information Technology, Kalyani, Computer Science and Engineering, Nadia, West Bengal, India
Pradipta Biswas
Affiliation:
Indian Institute of Science, Centre for Product Design and Manufacturing, Bengaluru, India
*
Author for correspondence: Abhishek Mukhopadhyay, E-mail: abhishek.gmit@gmail.com
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Abstract

Detecting and recognizing objects is one of the most important uses of vision systems in nature and is consequently highly evolved. This paper aims to accurately detect an object using its shape and color information from a complex background. In particular, we evaluated our algorithm to detect 19 different integrated circuits (IC) from 10 different printed circuit boards (PCB) of different colors. We have compared three different shape descriptors for four different color space models. We have evaluated shape detection algorithms in different lighting conditions (indoor, outdoor, and controlled light source) to find suitable illumination for image acquisition. We undertook statistical hypothesis testing to find the effect of color space models and shape descriptors on the accuracy, false positive and false negative rates. While measuring accuracy, we have noted that L*a*b* color space is significantly worse, and the best result is obtained in YCbCr color space using bounding box shape descriptors for 2500 Lux using LED.

Keywords

Design for manufacturinghuman–computer interactionman-machine systemsmanufacturing
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 4: Intelligent Interaction Design , November 2019 , pp. 389 - 398
Copyright
Copyright © Cambridge University Press 2019

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