Segmentation approach by learning: different image applications

H. Legal-Ayala, J. Facon
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Abstract

We present a new segmentation approach by thresholding based on learning strategy. This strategy is based only on one image and its ideal thresholded version. A decision matrix is generated from each pixel and each gray level. At the moment of new image segmentation, the best solution for each pixel is evaluated by means of K nearest neighbors in the decision matrix. Comparative tests were performed on signature, fingerprint and magnetic resonance images.
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通过学习分割方法:不同图像的应用
提出了一种基于学习策略的阈值分割方法。此策略仅基于一个图像及其理想阈值版本。从每个像素和每个灰度级生成决策矩阵。在进行新的图像分割时,通过决策矩阵中的K个最近邻来评估每个像素的最佳解。对签名、指纹和磁共振图像进行对比测试。
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