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Optical method and system for rapid identification of multiple refractive index materials using multiscale texture and color invariants

机译:使用多尺度纹理和颜色不变性快速识别多种折射率材料的光学方法和系统

摘要

An innovative optical system and method is disclosed for analyzing and uniquely identifying high-order refractive indices samples in a diverse population of nearly identical samples. The system and method are particularly suitable for ultra-fine materials having similar color, shape and features which are difficult to identify through conventional chemical, physical, electrical or optical methods due to a lack of distinguishing features. The invention discloses a uniquely configured optical system which employs polarized sample light passing through a full wave compensation plate, a linear polarizer analyzer and a quarter wave retardation plate for producing vivid color bi-refringence pattern images which uniquely identify high-order refractive indices samples in a diverse population of nearly visually identical samples. The resultant patterns display very subtle differences between species which are frequently indiscernable by conventional microscopy methods. When these images are analyzed with a trainable with a statistical learning model, such as a soft-margin support vector machine with a Gaussian RBF kernel, good discrimination is obtained on a feature set extracted from Gabor wavelet transforms and color distribution angles of each image. By constraining the Gabor center frequencies to be low, the resulting system can attain classification accuracy in excess of 90% for vertically oriented images, and in excess of 80% for randomly oriented images.
机译:公开了一种创新的光学系统和方法,用于分析和唯一地识别几乎相同的样本的多样性中的高阶折射率样本。该系统和方法特别适用于具有相似的颜色,形状和特征的超细材料,这些材料由于缺乏区别特征而难以通过常规化学,物理,电或光学方法来识别。本发明公开了一种独特配置的光学系统,该光学系统使用穿过全波补偿板,线性偏振分析仪和四分之一波长延迟板的偏振样本光,以产生生动的彩色双折射图案图像,该图像独特地识别出高阶折射率样本。几乎在视觉上完全相同的样本的多样化群体。所得图案显示出物种之间非常细微的差异,这通常是常规显微镜方法无法分辨的。当使用可训练的统计学习模型(例如具有高斯RBF核的软边距支持向量机)对这些图像进行分析时,在从Gabor小波变换和每个图像的颜色分布角度提取的特征集上可以获得良好的区分度。通过将Gabor中心频率限制为较低,对于垂直定向的图像,所得系统可以获得超过90%的分类精度,而对于随机定向的图像,则可以达到80%以上的分类精度。

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