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Region Merging Image Segmentation Algorithm Based on Boundary Extraction

机译:基于边界提取的区域合并图像分割算法

摘要

A Region Merging image segmentation algorithm based on boundary extraction is disclosed, comprising the steps of calculating a gradient image, extracting a boundary and carrying out initial segmentation and Region Merging, wherein the initial segmentation can be omitted. In the Region Merging process, the ratio of the length of boundary extraction result lies on the adjacent edge between adjacent regions to the length of the adjacent edge is taken as the merging cost, the regions are merged according to the ascending order of the mean gradient value in the region, and a texture difference evaluation mechanism is introduced to remove the error segmentation. The algorithm solves the problems of the other current segmentation algorithms, such as over-segmentation, easy to be influenced by noise and illumination, large in computation and memory consumption, in need of a large number of samples being marked manually and the like. Besides, all regions or all categories achieve the best segmentation result on one final segmentation result. These advantageous features can reduce the computational resource consumption of subsequent tasks and improve their processing effect.
机译:公开了一种基于边界提取的区域合并图像分割算法,包括计算梯度图像的步骤,提取边界并执行初始分割和区域合并,其中可以省略初始分割。在该区域合并过程中,将边界提取结果的长度与相邻区域之间的相邻边缘的比例呈现为相邻边缘的长度,作为合并成本,根据平均梯度的升序合并区域引入该区域的值,并引入纹理差异评估机制以删除错误分段。该算法解决了其他电流分割算法的问题,例如过分分割,易于受到噪声和照明的易受影响,计算和存储器消耗量大,需要手动标记大量样本等。此外,所有地区或所有类别都达到了一个最终分割结果的最佳分割结果。这些有利的特征可以减少随后任务的计算资源消耗并提高其处理效果。

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