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- W2625171973 abstract "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." @default.
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- W2625171973 date "2017-04-01" @default.
- W2625171973 modified "2023-10-16" @default.
- W2625171973 title "Selecting candidate regions of clustered tomato fruits under complex greenhouse scenes using RGB-D data" @default.
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- W2625171973 doi "https://doi.org/10.1109/iccar.2017.7942724" @default.
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