Enhancement of Image Resolution by Binarization

Image segmentation is one of the principal approaches of image processing. The choice of the most appropriate Binarization algorithm for each case proved to be a very interesting procedure itself. In this paper, we have done the comparison study between the various algorithms based on Binarization algorithms and propose a methodologies for the validation of Binarization algorithms. In this work we have developed two novel algorithms to determine threshold values for the pixels value of the gray scale image. The performance estimation of the algorithm utilizes test images with, the evaluation metrics for Binarization of textual and synthetic images. We have achieved better resolution of the image by using the Binarization method of optimum thresholding techniques.Comment: 5 pages, 8 figure

Persian Heritage Image Binarization Competition (PHIBC 2012)

The first competition on the binarization of historical Persian documents and manuscripts (PHIBC 2012) has been organized in conjunction with the first Iranian conference on pattern recognition and image analysis (PRIA 2013). The main objective of PHIBC 2012 is to evaluate performance of the binarization methodologies, when applied on the Persian heritage images. This paper provides a report on the methodology and performance of the three submitted algorithms based on evaluation measures has been used.Comment: 4 pages, 2 figures, conferenc

ICDAR2003 Page Segmentation Competition

There is a significant need to objectively evaluate layout analysis (page segmentation and region classification) methods. This paper describes the Page Segmentation Competition (modus operandi, dataset and evaluation criteria) held in the context of ICDAR2003 and presents the results of the evaluation of the candidate methods. The main objective of the competition was to evaluate such methods using scanned documents from commonly-occurring publications. The results indicate that although methods seem to be maturing, there is still a considerable need to develop robust methods that deal with everyday documents

Shape and Texture Combined Face Recognition for Detection of Forged ID Documents

This paper proposes a face recognition system that can be used to effectively match a face image scanned from an identity (ID) doc-ument against the face image stored in the biometric chip of such a document. The purpose of this specific face recognition algorithm is to aid the automatic detection of forged ID documents where the photography printed on the document’s surface has been altered or replaced. The proposed algorithm uses a novel combination of texture and shape features together with sub-space representation techniques. In addition, the robustness of the proposed algorithm when dealing with more general face recognition tasks has been proven with the Good, the Bad & the Ugly (GBU) dataset, one of the most challenging datasets containing frontal faces. The proposed algorithm has been complement-ed with a novel method that adopts two operating points to enhance the reliability of the algorithm’s final verification decision.Final Accepted Versio

Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images

Cell segmentation in microscopy is a challenging problem, since cells are often asymmetric and densely packed. This becomes particularly challenging for extremely large images, since manual intervention and processing time can make segmentation intractable. In this paper, we present an efficient and highly parallel formulation for symmetric three-dimensional (3D) contour evolution that extends previous work on fast two-dimensional active contours. We provide a formulation for optimization on 3D images, as well as a strategy for accelerating computation on consumer graphics hardware. The proposed software takes advantage of Monte-Carlo sampling schemes in order to speed up convergence and reduce thread divergence. Experimental results show that this method provides superior performance for large 2D and 3D cell segmentation tasks when compared to existing methods on large 3D brain images

Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets