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Automated design of image recognition in capturing environment
Author(s) -
Ogata Taiki,
Yukisawa Taigo,
Arai Tamio,
Ueyama Tsuyoshi,
Takada Toshiyuki,
Ota Jun
Publication year - 2017
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22551
Subject(s) - artificial intelligence , computer vision , computer science , preprocessor , process (computing) , rgb color model , image (mathematics) , digital image processing , image processing , binary image , pattern recognition (psychology) , operating system
This study aims at automating the design of the image‐recognition algorithm and the image‐acquisition environment for an industrial picking system. Here for the image‐recognition algorithm, a preprocessing image parameter and a discriminator using local features in images are targeted. For the image‐acquisition environment, the camera distance from the target objects and the illumination strength of each RGB color are considered. The problem is formulated as an optimization problem, and a method is proposed to derive solutions using a two‐phase random multistart local optimization for the image‐acquisition environment and the image‐recognition algorithm. In addition, experiment‐based optimization is made to deal with the uncertainty of the capturing environment. Furthermore, positions and angles are considered in a robot coordinate system to simplify the image‐acquisition process. The three evaluation experiments targeting objects with different surface characteristics are conducted. The results show that the proposed system successfully designed parameter sets for the image‐acquisition environment and the image‐recognition algorithm that suited the characteristics of the target objects. The object recognition rate, that is, F measure , is 1 for all objects in all the three experiments.