Abstract
In automatic visual inspection, the object image subspace should be segmented and matched quickly so that the affine relationship can be built between the template image and the sample image. When the interference is strong and the illumination is uneven, for example in an industrial application, this can make it difficult to obtain an objects subspace quickly and accurately in real-time. In this paper, a novel strategy is proposed to adopt discrete radial search paths instead of searching all points in an image. Therefore, the searching time can be substantially reduced. In order to reduce the influence coming from the industrial environment, the paper proposes another method that is local energy level set segmentation, which can locate the object subspace more efficiently and accurately. The detection of “crown caps” is presented as an example in this paper. Detection effects and computing time are compared between several detection methods, and the mechanisms of inspection have also been analyzed.
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Wen-Ju Zhou received the B. Sc. and M. Sc. degrees from the Shandong Normal University, China in 1990 and 2005, and the Ph. D. degree from the Shanghai University, China in 2014. He is now a robotic engineer in School of Computer Science and Electronic Engineering, University of Essex, UK, and the associate professor in the School of Information and Electronic Engineering, Ludong University, China.
His research interests include intelligent control, machine vision and the industry applications of the automation equipment.
ORCID iD: 0000-0001-9528-7590
Zi-Xiang Fei graduated from the University of York, UK in 2014 with a M. Sc. degree in communications engineering. He is currently preparing to further his study as a Ph. D. student in UK.
Huo-Sheng Hu received the M. Sc. degree in industrial automation from Central South University, China in 1982 and the Ph.D. degree in robotics from the University of Oxford, UK in 1993. He is currently a professor with the School of Computer Science and Electronic Engineering, University of Essex, UK, leading the Human Centered Robotics Group. He has published more than 370 research articles in journals, books and conference proceedings. He is a fellow of Institute of Engineering & Technology and Institution of Measurement & Control in the UK, a senior member of IEEE and ACM, and a chartered engineer. He is currently the editor-in-chief for International Journal of Automation and Computing, founding editor-in-chief for Robotics Journal and an executive editor for International Journal of Mechatronics and Automation.
His research interests include autonomous robots, human–robot interaction, multi-robot collaboration, embedded systems, pervasive computing, sensor integration, intelligent control, cognitive robotics, and networked robots.
Li Liu received the B. Sc. degree in physics from Ocean University of China, China in 1985, and the M. Sc. degree in condensed matter physics from Jinan University, China in 1991. She is now a lecturer with School of Information and Electronic Engineering, Ludong University, China.
Her research interests include image processing, image registration and patter recognition.
Li Liu graduated from Qufu Normal University, China in 2004. She received the M. Sc. degree from Dalian Maritime University, China in 2007. She is currently a Ph. D. candidate in Shanghai University, China, and she also is a lecturer at Ludong University, in the School of Information Science and Electrical Engineering.
Her research interests include machine vision, image processing and pattern recognition.
Peter James Smith graduated from the University of Essex, England in 2001 with a B.Eng. (Hons.) in telecommunications systems engineering. He is currently working as a senior research engineer for Vitec Videocom in the area of robot navigation.
An erratum to this article is available at http://dx.doi.org/10.1007/s11633-016-1032-0.
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Zhou, WJ., Fei, ZX., Hu, HS. et al. Real-time object subspace searching based on discrete searching paths and local energy. Int. J. Autom. Comput. 13, 99–107 (2016). https://doi.org/10.1007/s11633-015-0946-2
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DOI: https://doi.org/10.1007/s11633-015-0946-2