Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity.

/pdf/adaptive-thresholding-technique-for-retinal-vessel-te90j0qnny.pdf

Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information

We present, in this paper, the FAIR algorithm: a fast algorithm for document image restoration. This algorithm has been submitted to different contests where it showed good performance in comparison to the state of the art. In addition, this method is scale invariant and fast enough to be used in real-time applications. The method is based on a double-threshold edge detection approach that makes it possible to detect small details while remaining robust against noise. The performance of the proposition is evaluated on several types of degraded document images where considerable background noise or variation in contrast and illumination exist.

/pdf/fair-a-fast-algorithm-for-document-image-restoration-1brwjwarl5.pdf

FAIR: A Fast Algorithm for Document Image Restoration

A new binarization method for non-uniform illuminated document images

https://www.cin.ufpe.br/~cabm/visao/artigos/Transition%20pixel%20A%20concept%20for%20binarization%20based%20on%20edge%20detection%20and%20gray-intensity%20histograms.pdf

Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms

We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probability (MAP-MRF) estimation. While the conventional algorithms use the decision based on the thresholding, the proposed algorithm makes a soft decision based on the probabilistic model. To work with the MAP-MRF framework we formulate an energy function by a likelihood model and a generalized Potts prior model. Then we construct a graph for the energy, and obtain the optimized result by using the well-known graph cut algorithm. Experimental results show that our approach is more robust to various types of images than the previous hard decision approaches.

MAP-MRF approach for binarization of degraded document image

Noises and complex backgrounds often make the thresholding of degraded document images a difficult job. In this paper, we propose a new threshold selection method to handle severely degraded document images. First, multiscale image description is adopted to analyze the image edge. From it, edge pixel pair information is derived and recorded by a graylevel co-occurrence matrix. An appropriate threshold value is chosen by measuring the edge pixel pair co-occurrence matrix. The new method is tested with degraded document images. The experimental results show it is resistant to noises and complex backgrounds.

http://ieeexplore.ieee.org/iel5/10526/33307/01575610.pdf

A Threshlod Selection Method Based on Multiscale and Graylevel Co-occurrence

Noise and complex backgrounds often make the thresholding of degraded document images difficult. In this paper, we propose a new threshold selection method to handle severely degraded document images. First, multiscale image description is adopted to analyze the image edge. From this, edge pixel pair information is derived and recorded by a graylevel co-occurrence matrix. An appropriate threshold value is chosen by measuring the edge pixel pair co-occurrence matrix. The new method is tested with degraded document images. The experimental results show it is resistant to noise and complex backgrounds.

A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis

Segmentation is an essential part of a recognition system. It is difficult to handle touching characters, especially for italic fonts. We present a method to achieve the accurate segmentation of touching italic characters. It is free of slant correction, so extra noises will not be introduced. We use slant projection and contour analysis to find the segmentation points. Then the shortest path approach is adopted for accurately locating the cut path of each candidate segmentation point. Based on dynamic programming, we can find the best segmentation result from those cut paths. By this method, we can improve the recognition rate on italic fonts.

Yun Li

Papers

A Threshlod Selection Method Based on Multiscale and Graylevel Co-occurrence

A threshlod selection method based on multiscale and graylevel co-occurrence matrix analysis

A segmentation method for touching italic characters