Image Classification with Indicator Kriging Error Comparison

机译：图像分类与指示灯kriging错误比较

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Methods for classification of images are of an important research area in image processing and pattern recognition. In particular, image classification is playing an increasingly important role in medicine and biology with respect to medical diagnoses and drug discovery, respectively. This paper presents a new method for image classification based on the frameworks of fuzzy sets and geostatistics. The proposed method was applied to the automated detection of regions of mitochondria in microscope images. The high correction rate of detecting the locations of the mitochondria in a complex environment obtained from the proposed method suggests its effectiveness and its better performance than several other existing algorithms.

机译：图像分类方法是图像处理和模式识别中的重要研究区域。特别是，图像分类分别在医学和生物学中发挥着越来越重要的作用和医学诊断和药物发现。本文介绍了基于模糊集和地统计数据框架的图像分类的新方法。将所提出的方法应用于显微镜图像中线粒体区域的自动检测。检测从该方法获得的复杂环境中检测线粒体位置的高校正率表明其有效性及其比其他几种现有算法更好的性能。

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《International Conference on Image and Signal Processing》|2014年||共8页
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Tuan D. Pham;
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中图分类 TN911.73-53;
关键词
Image classification; Fuzzy sets; Indicator kriging; Uncertainty;

机译：图像分类;模糊套;指示器Kriging;不确定性;

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Image Classification with Indicator Kriging Error Comparison

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