Abstract
Object recognition system plays a vital role in controlling the robotic arm for applications such as picking and placing of objects. This paper is directed towards the development of the image processing algorithm which is the main process of pick and place robotic arm control system. In this paper, soft drink can objects such as “Shark”, “Burn”, “Sprite” and “100 Plus” are recognized. When the user specifies a soft drink can object, the system tries to recognize the object automatically. In the system, the target object region and the motion of the object are firstly detected using Template Matching (Normalized Cross Correlation) based on YCbCr color space. The detected image is segmented into five parts horizontally to extract color features. In feature extraction step, mean color and Hue values are extracted from each segmented image. And then, Adaptive Neural Fuzzy Inference System (ANFIS) is employed to recognize the target object based on the color features. After recognizing the user specified object, the robotic arm pick and place it in the target region. Experimental results show that the proposed method is efficiently able to identify and recognize soft drink can objects.
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Acknowledgments
Firstly, I would like to thankful to Dr. Aung Win, Rector, University of Technology (Yatanarpon Cyber City), for his supporting to develop this system successfully. Secondly, I would like to appreciate Dr. Soe Soe Khaing, Pro-Rector, Leader of Research Development Team for Domestic Pick and Place Robotic Arm Control System, University of Technology (Yatanarpon Cyber City), for her vision, chosen, giving valuable advices and guidance for preparation of this article. And then, I wish to express my deepest gratitude to my teacher Dr. Hninn Aye Thant, Professor, Department of Information Science and Technology, University of Technology (Yatanarpon Cyber City), for her advice. I am also grateful to Dr. Thuzar Tint, Lecturer, University of Technology (Yatanarpon Cyber City), for giving us valuable advices. Last but not least, many thanks are extended to all persons who directly and indirectly contributed towards the success of this paper.
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Sat, A.K., Tint, T. (2019). Object Detection and Recognition System for Pick and Place Robot. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_35
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DOI: https://doi.org/10.1007/978-981-13-0869-7_35
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