Energy-Efficient Illumination Control Using Image Parameters in a Machine Vision Environment for Optimum Surface Texture
Synergy of optimum illumination and image processing techniques is a very important aspect which needs to be incorporated in a machine vision environment to improve the durability of the lighting unit and also to conserve power requirements. This research
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stract Synergy of optimum illumination and image processing techniques is a very important aspect which needs to be incorporated in a machine vision environment to improve the durability of the lighting unit and also to conserve power requirements. This research work presents a novel way to optimize lighting requirements in a machine vision system using image feature analysis and image processing algorithms for texture identification. The practical implementation could be considered for automated machine vision environment for object surface inspection and quality monitoring.
Keywords Illumination control Image processing Canny algorithm Surface texture Fourier transform Harris corner points Intensity maps
1 Introduction Nowadays, in many of the automated manufacturing and packaging industries, machine vision-based applications have a vital role in defining the product quality and finish. The need for a vision-based equipment in any manufacturing and inspection process is a necessity due to the fact that human visual system has limitations considering the environment conditions, robustness of deciphering an image, ability to focus only on the ROI, pixel level object identification, etc. All these issues can be clearly addressed as per the user requirements, by using a right combination of lens, camera, lighting equipment, corresponding data acquisition hardware, and image processing software. With the advancement of automation technology, production and inspection process in manufacturing companies have become simplified using online visual inspection methods where in the scope of R. Manish (&) S. Denis Ashok Department of Design and Automation, VIT University, Vellore, India e-mail: [email protected] S. Denis Ashok e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 A. Konkani et al. (eds.), Advances in Systems, Control and Automation, Lecture Notes in Electrical Engineering 442, https://doi.org/10.1007/978-981-10-4762-6_16
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human involvement all the stages are minimized. The production cycle, i.e., from part production to inspection and packaging, is completely controlled and monitored by a central server zone. Many researchers have contributed to the varied fields consisting of machine vision applications. Chan et al. [1] have presented the scope of machine vision applications in industries. Kumar [2] researched upon the fabric defect detection applications in textile industry. Ke et al. [3] researched upon the online surface inspection in steel industries. Yu et al. [4] researched upon the micro-tool wear measurements using machine vision. Duan et al. [5] researched, developed, and successfully implemented a prototype which can detect bottle defects using machine vision application. Zhao et al. [6] researched and have presented work for identifying wooden sample species identification based upon the shape and texture of the wooden surfaces. Jin-Cong et al. [7] have investigated upon the wooden surface feature defect detection based upo
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