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首页> 外文会议>Agriculture and Hydrology Applications of Remote Sensing; Proceedings of SPIE-The International Society for Optical Engineering; vol.6411 >An Automatic Segmentation Method for Multi-tomatoes Image under Complicated Natural Background
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An Automatic Segmentation Method for Multi-tomatoes Image under Complicated Natural Background

机译：复杂自然背景下多番茄图像的自动分割方法

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摘要

It is a fundamental work to realize intelligent fruit-picking that mature fruits are distinguished from complicated backgrounds and determined their three-dimensional location. Various methods for fruit identification can be found from the literatures. However, surprisingly little attention has been paid to image segmentation of multi-fruits which growth states are separated, connected, overlapped and partially covered by branches and leaves of plant under the natural illumination condition. In this paper we present an automatic segmentation method that comprises of three main steps. Firstly, Red and Green component image are extracted from RGB color image, and Green component subtracted from Red component gives RG of chromatic aberration gray-level image. Gray-level value between objects and background has obviously difference in RG image. By the feature, Ostu's threshold method is applied to do adaptive RG image segmentation. And then, marker-controlled watershed segmentation based on morphological grayscale reconstruction is applied into Red component image to search boundary of connected or overlapped tomatoes. Finally, intersection operation is done by operation results of above two steps to get binary image of final segmentation. The tests show that the automatic segmentation method has satisfactory effect upon multi-tomatoes image of various growth states under the natural illumination condition. Meanwhile, it has very robust for different maturity of multi-tomatoes image.
机译：实现成熟水果与复杂背景的区别并确定其三维位置，是实现智能水果采摘的基础工作。可以从文献中找到用于水果鉴定的各种方法。然而，令人惊讶的是，很少有人关注在自然光照条件下生长状态被植物的树枝和树叶分离，连接，重叠和部分覆盖的多种水果的图像分割。在本文中，我们提出了一种自动分割方法，该方法包括三个主要步骤。首先，从RGB彩色图像中提取红色和绿色分量图像，然后从红色分量中减去绿色分量，得到色差灰度图像的RG。物体和背景之间的灰度值在RG图像中有明显差异。通过此功能，奥斯图的阈值方法被应用于自适应RG图像分割。然后，将基于形态灰度重构的标记控制的分水岭分割应用于红色分量图像中，以搜索连接或重叠的番茄的边界。最后，通过以上两步的运算结果进行交点运算，得到最终分割的二值图像。实验表明，在自然光照条件下，自动分割方法对不同生长状态的多番茄图像具有令人满意的效果。同时，它对于不同成熟度的多番茄图像具有非常强的鲁棒性。

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