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Surface fitting for individual image thresholding and beyond

机译：表面拟合，可进行单独的图像阈值处理

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摘要

In this study, the authors propose a novel algorithm for background??foreground segmentation. The work is motivated by the need for information about the background that is obscured by objects, in order to achieve accurate segmentation. The algorithm utilises the principle of estimating the occluded background by surface fitting. Edge detection methods are used to detect boundaries between foreground and background, identifying background points as well as foreground points. This categorisation will guarantee that most points used for surface fitting are from the same category and thus the proposed surface fitting with random sample consensus (RANSAC) algorithm will produce an accurate estimate of the surface. The authors algorithm has been applied to the real-world applications of segmenting plant images with inhomogeneous but smooth background and measuring the relative temperature of plants. Comparisons with experimental results show that the proposed algorithm is able to reduce significantly background inhomogeneities in infra-red images for the accurate estimation of temperature differences between background and plants, which provides important clues for fast and cheap genetic screening. The proposed algorithm is also able to overcome the intensity inhomogeneities for accurate image segmentation, particularly for plant root image segmentation with the preservation of lateral plant roots.
机译：在这项研究中，作者提出了一种新的背景前景分割算法。为了获得准确的分割，需要对象遮盖的有关背景的信息来推动这项工作。该算法利用通过表面拟合估计被遮挡的背景的原理。边缘检测方法用于检测前景和背景之间的边界，识别背景点和前景点。这种分类将确保用于曲面拟合的大多数点都来自同一类别，因此，建议的带有随机样本共识（RANSAC）算法的曲面拟合将产生对曲面的准确估计。作者算法已应用于具有不均匀但平滑背景的植物图像分割和测量植物相对温度的实际应用中。与实验结果的比较表明，所提出的算法能够显着减少红外图像中的背景不均匀性，从而准确估算背景与植物之间的温度差，这为快速，廉价的基因筛选提供了重要的线索。所提出的算法还能够克服强度不均匀性，以进行精确的图像分割，尤其是对于保留植物侧生根的植物根图像分割。

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    《Image Processing, IET》 |2013年第6期|596-605|共10页
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