Online Measuring and Size Sorting for Perillae Based on Machine Vision Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175xe2x80x89ms, 1.48xe2x80x89mm, 0.07xe2x80x89mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.

Binpicking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation * The use of robots in automated factories requires accurate binpicking to ensure that objects are correctly identified and selected. In the case of objects with multiple reflections from their surfaces, this is a challenging task. We attempted to address this problem by developing a 3D measurement method based on a Light Transport Matrix (LTM), which can be applied to shiny objects or semitransparent objects. The study presented herein evaluates the accuracy of the proposed method as well as the method for 3D pose estimation we previously reported, by examining a binpicking task, which is a wellknown robot application in factory automation. There is considerable demand for automated binpicking systems for general objects. However, in the use of costeffective measurement systems, some objects such as shiny metallic objects continue to prove problematic in terms of binpicking because of the difficulty to measure their shapes accurately. Our 3D measurement method uses only a projectorcamera system; thus, it is costeffective, and it does not require any special optical system. It is based on fast LTM sparse estimation. We previously demonstrated that this approach can measure the 3D shape of metallic objects and showed that our pose estimation method is applicable to binpicking. However, we did not verify its accuracy with the application of 3D robot vision. In this study, we integrate these two methods, and demonstrate that our 3D measurement method, in combination with our pose estimation work, can successfully accomplish binpicking tasks involving shiny metallic industrial objects. Ultimately, we achieved 100 % picking success for 5 scenes including 15 pieces. We concluded that our proposed methods are sufficiently accurate to carry out binpicking tasks in automated factory environments.