Multilevel minimum cross entropy threshold selection based on particle swarm optimization

Abstract Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications

Document image restoration - For document images scanned from bound volumes -

Ph.DDOCTOR OF PHILOSOPH

Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Text Segmentation in Web Images Using Colour Perception and Topological Features

The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methods’ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented

Analytical methods fort he study of color in digital images

La descripció qualitativa dels colors que composen una imatge digital és una tasca molt senzilla pel sistema visual humà. Per un ordinador aquesta tasca involucra una gran quantitat de qüestions i de dades que la converteixen en una operació de gran complexitat. En aquesta tesi desenvolupam un mètode automàtic per a la construcció d’una paleta de colors d’una imatge digital, intentant respondre a les diferents qüestions que se’ns plantegen quan treballam amb colors a dins el món computacional. El desenvolupament d’aquest mètode suposa l’obtenció d’un algorisme automàtic de segmentació d’histogrames, el qual és construït en detall a la tesi i diferents aplicacions del mateix son donades. Finalment, també s’explica el funcionament de CProcess, un ‘software’ amigable desenvolupat per a la fàcil comprensió del color

Geometric Layout Analysis of Scanned Documents

Layout analysis--the division of page images into text blocks, lines, and determination of their reading order--is a major performance limiting step in large scale document digitization projects. This thesis addresses this problem in several ways: it presents new performance measures to identify important classes of layout errors, evaluates the performance of state-of-the-art layout analysis algorithms, presents a number of methods to reduce the error rate and catastrophic failures occurring during layout analysis, and develops a statistically motivated, trainable layout analysis system that addresses the needs of large-scale document analysis applications. An overview of the key contributions of this thesis is as follows. First, this thesis presents an efficient local adaptive thresholding algorithm that yields the same quality of binarization as that of state-of-the-art local binarization methods, but runs in time close to that of global thresholding methods, independent of the local window size. Tests on the UW-1 dataset demonstrate a 20-fold speedup compared to traditional local thresholding techniques. Then, this thesis presents a new perspective for document image cleanup. Instead of trying to explicitly detect and remove marginal noise, the approach focuses on locating the page frame, i.e. the actual page contents area. A geometric matching algorithm is presented to extract the page frame of a structured document. It is demonstrated that incorporating page frame detection step into document processing chain results in a reduction in OCR error rates from 4.3% to 1.7% (n=4,831,618 characters) on the UW-III dataset and layout-based retrieval error rates from 7.5% to 5.3% (n=815 documents) on the MARG dataset. The performance of six widely used page segmentation algorithms (x-y cut, smearing, whitespace analysis, constrained text-line finding, docstrum, and Voronoi) on the UW-III database is evaluated in this work using a state-of-the-art evaluation methodology. It is shown that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. Thus, a vectorial score is introduced that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and mis-segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous schemes, this evaluation method has a canonical representation of ground truth data and guarantees pixel-accurate evaluation results for arbitrary region shapes. Based on a detailed analysis of the errors made by different page segmentation algorithms, this thesis presents a novel combination of the line-based approach by Breuel with the area-based approach of Baird which solves the over-segmentation problem in area-based approaches. This new approach achieves a mean text-line extraction error rate of 4.4% (n=878 documents) on the UW-III dataset, which is the lowest among the analyzed algorithms. This thesis also describes a simple, fast, and accurate system for document image zone classification that results from a detailed comparative analysis of performance of widely used features in document analysis and content-based image retrieval. Using a novel combination of known algorithms, an error rate of 1.46% (n=13,811 zones) is achieved on the UW-III dataset in comparison to a state-of-the-art system that reports an error rate of 1.55% (n=24,177 zones) using more complicated techniques. In addition to layout analysis of Roman script documents, this work also presents the first high-performance layout analysis method for Urdu script. For that purpose a geometric text-line model for Urdu script is presented. It is shown that the method can accurately extract Urdu text-lines from documents of different layouts like prose books, poetry books, magazines, and newspapers. Finally, this thesis presents a novel algorithm for probabilistic layout analysis that specifically addresses the needs of large-scale digitization projects. The presented approach models known page layouts as a structural mixture model. A probabilistic matching algorithm is presented that gives multiple interpretations of input layout with associated probabilities. An algorithm based on A* search is presented for finding the most likely layout of a page, given its structural layout model. For training layout models, an EM-like algorithm is presented that is capable of learning the geometric variability of layout structures from data, without the need for a page segmentation ground-truth. Evaluation of the algorithm on documents from the MARG dataset shows an accuracy of above 95% for geometric layout analysis.Geometrische Layout-Analyse von gescannten Dokumente