首页> 外文会议>IEEE Workshop on Machine Learning for Signal Processing >PIT PATTERN CLASSIFICATION OF ZOOM-ENDOSCOPICAL COLON IMAGES USING EVOLVED FOURIER FEATURE VECTORS
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PIT PATTERN CLASSIFICATION OF ZOOM-ENDOSCOPICAL COLON IMAGES USING EVOLVED FOURIER FEATURE VECTORS

机译:使用演进傅立叶特征向量的变焦内窥镜冒号图像的坑模式分类

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This work describes an experimental study on the classification of images taken from colonoscopy. An emphasis is devoted to the procedure of finding features which allow an adequate classification. The proposed approach applies filters to the images' respective Fourier domains. Good configurations of these filters are obtained using a genetic algorithm, since the complexity of the configuration space is too high to find the optimum in reasonable time. The actual classification is done according to the pit pattern scheme and uses standard methods from statistical pattern recognition.
机译:这项工作描述了对来自结肠镜检查拍摄的图像分类的实验研究。强调致力于找到允许足够分类的特征的程序。该方法将过滤器应用于图像“相应的傅立叶域。使用遗传算法获得这些过滤器的良好配置,因为配置空间的复杂性太高,无法在合理的时间内找到最佳。实际分类是根据凹坑模式方案进行的,并使用统计模式识别的标准方法。

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