Digital Image Processing
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Practical colour constancy
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Edge and Corner Detection by Photometric Quasi-Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text segmentation in color images using tensor voting
Image and Vision Computing
Colour text segmentation in web images based on human perception
Image and Vision Computing
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
Natural enhancement of color image
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Con-text: text detection using background connectivity for fine-grained object classification
Proceedings of the 21st ACM international conference on Multimedia
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In this paper, we propose a novel method for detecting and segmenting text layers in complex images. This method is robust against degradations such as shadows, non-uniform illumination, low-contrast, large signaldependent noise, smear and strain. The proposed method first uses a geodesic transform based on a morphological reconstruction technique to remove dark/light structures connected to the borders of the image and to emphasize on objects in center of the image. Next uses a method based on difference of gamma functions approximated by the Generalized Extreme Value Distribution (GEVD) to find a correct threshold for binarization. The main function of this GEVD is to find the optimum threshold value for image binarization relatively to a significance level. The significance levels are defined in function of the background complexity. In this paper, we show that this method is much simpler than other methods for text binarization and produces better text extraction results on degraded documents and natural scene images.