从树木图像中分割椰子作物束

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI:10.1109/CCIP.2016.7802865

S. Siddesha, S. Niranjan, V. N. Manjunath Aradhya

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引用次数: 3

摘要

收获是作物管理中非常关键的阶段之一。在适当的时候收割庄稼可以提高质量。本文从树状图像中对椰子作物束进行了分割。探索了基于颜色的K-Means聚类、标记控制分水岭、生长切割和基于最大相似度的区域合并(MSRM)等不同的分割方法。实验使用200张图像的数据集进行演示。在这些方法中，MSRM提供了良好的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Segmentation of coconut crop bunch from tree images

Harvesting is one of the very crucial stages in crop management. Harvesting the crop at proper time will enhance the quality. In this paper we segmented the coconut crop bunch from tree image. Different segmentation methods like, Color based K-Means clustering, Marker controlled watershed, Grow-cut and Maximum Similarity based Region Merging (MSRM) are explored. Experimentation conducted using a dataset of 200 images for demonstration. Out of these methods the MSRM provides good result.

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来源期刊

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)

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