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Text Segmentation from Images with Various Light Conditions Based on Gaussian Mixture Model
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 Title & Authors
Text Segmentation from Images with Various Light Conditions Based on Gaussian Mixture Model
Tran, Khoa Anh; Lee, Gueesang;
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Standard Gaussian Mixture Model (GMM) is a well-known method for image segmentation. However, one of its problems is that we consider the pixel as independent to each other, which can cause the segmentation results sensitive to noise. It explains why some of existing algorithms still cannot segment texts from the background clearly. Therefore, we present a new method in which we incorporate the spatial relationship between a pixel and its neighbors inside windows to segment the text. Our approach works well with images containing texts, which has different sizes, shapes or colors in case of light changes or complex background. Experimental results demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation compared to other methods.
Gaussian Mixture Model (GMM);Image Segmentation;Spatial Neighboring Relationships;Expectation Maximization;
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