Comparing shape descriptor methods for different color space and lighting conditions. Issue 4 (4th November 2019)

• Record Type: Journal Article
• Authors: Mukhopadhyay, Abhishek; Mukherjee, Imon; Biswas, Pradipta
• Editors: Biswas, Pradipta; Orero, Pilar; Sezgin, Metin
• Is Part Of: AI EDAM. Volume 33:Issue 4(2019)
• Page Start: 389
• Page End: 398
• Publication Date: 2019-11-04
• Subjects: Design for manufacturing, -- human–computer interaction, -- man-machine systems, -- manufacturing; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060419000398
• Languages: English
• ISSNs: 0890-0604

Abstract

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.