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Analysis of image segmentation methods on Amrita's Indian side face profile database

机译:阿姆里塔(Amrita)印度人侧面资料数据库中的图像分割方法分析

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Amongst an array of research topics, image segmentation is the most challenging one. This along with image thresholding are the fundamental problems that arise in image processing. There are 2 main methods in image segmentation. They are automatic and manual. In the automatic method, we do not require any person to operate on the segmentation, whereas in the manual very minimal user interaction is required. It is proven that the interactive or the manual approach gives a better result than the automatic approach. This paper focuses on the comparisons and the implementations of the segmentation methods and their analysis. It also gives insights whether automatic or manual methods are better. The algorithms that we used here are modified Level Set algorithm, Gaussian Mixture model, Support Vector Machine. Finally, all the results are obtained and are compared and contrasted.
机译:在一系列研究主题中,图像分割是最具挑战性的一个。这连同图像阈值化是图像处理中出现的基本问题。图像分割有两种主要方法。它们是自动的和手动的。在自动方法中,我们不需要任何人进行分割,而在手动方法中,则需要非常少的用户交互。事实证明,交互式或手动方法比自动方法具有更好的效果。本文着重于分割方法及其分析的比较和实现。它还提供了自动或手动方法更好的见解。我们在这里使用的算法是改进的水平集算法,高斯混合模型,支持向量机。最后,获得所有结果并进行比较和对比。

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