The extraction of characters on dated color postcards

[[abstract]]A novel scheme is proposed to extract characters from dated postcards. The illustrations of the postcards appear in various languages and colors embedded in different backgrounds. Due to reproduction and uneven illumination, these characters suffer a severe degradation and hence extracting characters using conventional methods becomes difficult. A morphological operation is proposed to remove irrelevant backgrounds that are connecting to border edges of the postcards. As a result, characters become the most obvious objects. Followed by horizontal and vertical projections, the exact locations of the characters can be located. The proposed scheme has been executed on a set of color images of postcards and proved its efficacy[[conferencetype]]國際[[conferencedate]]20040627~20040630[[booktype]]紙本[[conferencelocation]]Taipei, Taiwa

Effect of “Ground Truth” on Image Binarization

Image binarization has a large effect on the rest of the document image analysis processes in character recognition. Algorithm development is still a major focus of research. Evaluation of image binarization has been done by comparison of the result of OCR systems on images binarized by different methods. That has been criticized in that the binarization alone is not evaluated, but rather how it interacts with the downstream processes. Recently pixel accurate ground truth images have been introduced for use in binarization algorithm evaluation. This has been shown to be open to interpretation. The choice of binarization ground truth affects the binarization algorithm design, either directly if design is by automated algorithm trying to match the provided ground truth, or indirectly if human designers adjust their designs to perform better on the provided data. Three variations in pixel accurate ground truth were used to train a binarization classifier. The performance can vary significantly depending on choice of ground truth, which can influence binarization design choices

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Multifractal features of 3-D macropore structures of discretized X-ray CT of undisturbed soil columns

The objective of this work was to develop the multifractal description of soil porosity values (2-D sectional porosities) as a function of depth with data from binarized 2-D images that were obtained from X-ray CT scans of 12 water-saturated 20 cm-long soil columns with diameters of 7.5 cm. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690x690 pixels. The series corresponding to the percentage of void space of the sectional binarized images were recorded. These series of depth-dependent macroporosity values exhibited a well defined multifractal structure that was represented by the singularity and the Rényi spectra. We also parameterized the memory, or long range dependencies, in these series using the Hurst exponent and the multifractal model

Text Extraction on Chinese Paintings

[[abstract]]This paper presents a scheme to extract inscriptions from a traditional Chinese painting such that the inscriptions and the painting can be enjoyed or studied separately. A two phases morphological operation is used to remove most content of a painting (i.e. background) which makes inscriptions to become the principal object in the remaining image. Since inscriptions are written vertically, we use the alignment property to construct the center point map and use it to locate character lines. Character block is formed by clustering adjacent character lines. The proposed algorithm has been executed on a set of Chinese paintings and proved its efficacy.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20061008~20061011[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

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

Automatic Segmentation and Classification of Red and White Blood cells in Thin Blood Smear Slides

In this work we develop a system for automatic detection and classification of cytological images which plays an increasing important role in medical diagnosis. A primary aim of this work is the accurate segmentation of cytological images of blood smears and subsequent feature extraction, along with studying related classification problems such as the identification and counting of peripheral blood smear particles, and classification of white blood cell into types five. Our proposed approach benefits from powerful image processing techniques to perform complete blood count (CBC) without human intervention. The general framework in this blood smear analysis research is as follows. Firstly, a digital blood smear image is de-noised using optimized Bayesian non-local means filter to design a dependable cell counting system that may be used under different image capture conditions. Then an edge preservation technique with Kuwahara filter is used to recover degraded and blurred white blood cell boundaries in blood smear images while reducing the residual negative effect of noise in images. After denoising and edge enhancement, the next step is binarization using combination of Otsu and Niblack to separate the cells and stained background. Cells separation and counting is achieved by granulometry, advanced active contours without edges, and morphological operators with watershed algorithm. Following this is the recognition of different types of white blood cells (WBCs), and also red blood cells (RBCs) segmentation. Using three main types of features: shape, intensity, and texture invariant features in combination with a variety of classifiers is next step. The following features are used in this work: intensity histogram features, invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform features, Haralick and Tamura features. Next, different statistical approaches involving correlation, distribution and redundancy are used to measure of the dependency between a set of features and to select feature variables on the white blood cell classification. A global sensitivity analysis with random sampling-high dimensional model representation (RS-HDMR) which can deal with independent and dependent input feature variables is used to assess dominate discriminatory power and the reliability of feature which leads to an efficient feature selection. These feature selection results are compared in experiments with branch and bound method and with sequential forward selection (SFS), respectively. This work examines support vector machine (SVM) and Convolutional Neural Networks (LeNet5) in connection with white blood cell classification. Finally, white blood cell classification system is validated in experiments conducted on cytological images of normal poor quality blood smears. These experimental results are also assessed with ground truth manually obtained from medical experts