李长勇,曹其新.基于深度图像的蔬果形状特征提取[J].农业机械学报,2012,43(Z1):242-245. Li Changyong,Cao Qixin.Extraction Method of Shape Feature for Vegetables Based on Depth Image[J].Transactions of the Chinese Society for Agricultural Machinery,2012,43(Z1):242-245.
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Abstract:The method of shape feature extraction based on depth image for the classification of tomatoes shape was proposed. Firstly, the shape of tomatoes was separated from the background through the segmentation of image in color space. Secondly, the point cloud of tomatoes was obtained by unitizing a 3-D machine vision measuring device. In order to implement the shape feature extraction of tomatoes in the same scale, the depth values of tomatoes were normalized. The depth map of tomatoes was formed according to the result of segment and the depth information of tomato. Further the depth map was sampled in polar coordinates and the sampling data was re-plotted in Cartesian coordinates. Finally, the depth image was re-plotted in the form of the Fourier transform in the Cartesian coordinates. The generic Fourier descriptor(GFD)was calculated based on depth map. The descriptor was characterized by the invariance of transformation of translation, rotation and scaling. The GFD based on depth image and the general GFD were successively used in the experiment of tomato grading. The result showed that the mean accuracy of the former classification was up to 92% and higher than the latter. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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