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Image-based field boundary detection and identification

机译:基于图像的场边界检测与识别

摘要

Detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point. Images are clipped to align with the seed point and a bounding box around the seed point, and a mask is built by extracting edges of the field from the images. The workflow floods an area around the seed point that has pixels of a similar color, using the mask as an initial boundary. The flooded area is compared to threshold parameter values, which are tuned to refine the identified boundary. Flooded areas in multiple images are combined, and a boundary is built based on the combined flooded set. Manual, interactive tuning of floodfill areas allows for a separate boundary detection and identification workflow or for refinement of the automatic boundary detection workflow.
机译:基于相对于定义的种子点在不同时间捕获的田野图像的处理,在工作流中执行田野边界的检测和识别。裁剪图像以使其与种子点和种子点周围的边界框对齐,并通过从图像中提取场的边缘来构建蒙版。使用遮罩作为初始边界,工作流将种子点周围的区域淹没,该区域具有相似颜色的像素。将淹没区域与阈值参数值进行比较,对阈值参数值进行调整以细化所标识的边界。合并多个图像中的淹没区域,并基于合并的淹没集构建边界。手动,交互式地调整洪水区域可以实现单独的边界检测和识别工作流程,或者可以改进自动边界检测工作流程。

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