Selecting Candidate Regions of Clustered Tomato Fruits Under Complex Greenhouse Scenes Using RGB-D Data

文献类型： 外文期刊

第一作者： Qiu Quan

作者： Qiu Quan;Qiao Xiaojun;Jiang Kai;Feng Qingchun;Tian Lanlan

作者机构：

关键词： RGB-D;candidate region selection;clustered tomato fruits detection;complex greenhouse scene

期刊名称：2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR)

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年卷期： 2017 年

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收录情况： SCI

摘要： This paper presents a new candidate region selecting strategy for detecting clustered tomato fruits using RGB-D data, which is taken in complex greenhouse scenes. The strategy employs both color and depth information in the same Kinect sensing frame. First, one Kinect frame is processed to generate three separate images, including depth image, RGB color image, and S channel image in HSI color space. The images are processed with depth threshold, green enhancement and saturation threshold correspondingly. And a frontground mask image will be generated with the processed images through intersection and union operations. Second, depth gray image and color gray image will be filtered with the frontground mask. And candidate fruit regions can be obtained after open-close reconstruction. Experimental results show that the strategy can greatly cut down the regions of interests while keeping all the true fruit regions.

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  • 相关文献

[1]A New Algorithm for Greenhouse Corridor Edge Detection with RGB-D Data. Qiu Quan,Meng Zhijun,Qiu Quan,Meng Zhijun. 2015

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