Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.Comment: 16 pages, to appear in Springer's Lecture Notes in Computer Science volume "Pattern Recognition Applications and Methods 2013", part of series on Advances in Intelligent and Soft Computin

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

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Analyse d’images de documents patrimoniaux : une approche structurelle à base de texture

Over the last few years, there has been tremendous growth in digitizing collections of cultural heritage documents. Thus, many challenges and open issues have been raised, such as information retrieval in digital libraries or analyzing page content of historical books. Recently, an important need has emerged which consists in designing a computer-aided characterization and categorization tool, able to index or group historical digitized book pages according to several criteria, mainly the layout structure and/or typographic/graphical characteristics of the historical document image content. Thus, the work conducted in this thesis presents an automatic approach for characterization and categorization of historical book pages. The proposed approach is applicable to a large variety of ancient books. In addition, it does not assume a priori knowledge regarding document image layout and content. It is based on the use of texture and graph algorithms to provide a rich and holistic description of the layout and content of the analyzed book pages to characterize and categorize historical book pages. The categorization is based on the characterization of the digitized page content by texture, shape, geometric and topological descriptors. This characterization is represented by a structural signature. More precisely, the signature-based characterization approach consists of two main stages. The first stage is extracting homogeneous regions. Then, the second one is proposing a graph-based page signature which is based on the extracted homogeneous regions, reflecting its layout and content. Afterwards, by comparing the different obtained graph-based signatures using a graph-matching paradigm, the similarities of digitized historical book page layout and/or content can be deduced. Subsequently, book pages with similar layout and/or content can be categorized and grouped, and a table of contents/summary of the analyzed digitized historical book can be provided automatically. As a consequence, numerous signature-based applications (e.g. information retrieval in digital libraries according to several criteria, page categorization) can be implemented for managing effectively a corpus or collections of books. To illustrate the effectiveness of the proposed page signature, a detailed experimental evaluation has been conducted in this work for assessing two possible categorization applications, unsupervised page classification and page stream segmentation. In addition, the different steps of the proposed approach have been evaluated on a large variety of historical document images.Les récents progrès dans la numérisation des collections de documents patrimoniaux ont ravivé de nouveaux défis afin de garantir une conservation durable et de fournir un accès plus large aux documents anciens. En parallèle de la recherche d'information dans les bibliothèques numériques ou l'analyse du contenu des pages numérisées dans les ouvrages anciens, la caractérisation et la catégorisation des pages d'ouvrages anciens a connu récemment un regain d'intérêt. Les efforts se concentrent autant sur le développement d'outils rapides et automatiques de caractérisation et catégorisation des pages d'ouvrages anciens, capables de classer les pages d'un ouvrage numérisé en fonction de plusieurs critères, notamment la structure des mises en page et/ou les caractéristiques typographiques/graphiques du contenu de ces pages. Ainsi, dans le cadre de cette thèse, nous proposons une approche permettant la caractérisation et la catégorisation automatiques des pages d'un ouvrage ancien. L'approche proposée se veut indépendante de la structure et du contenu de l'ouvrage analysé. Le principal avantage de ce travail réside dans le fait que l'approche s'affranchit des connaissances préalables, que ce soit concernant le contenu du document ou sa structure. Elle est basée sur une analyse des descripteurs de texture et une représentation structurelle en graphe afin de fournir une description riche permettant une catégorisation à partir du contenu graphique (capturé par la texture) et des mises en page (représentées par des graphes). En effet, cette catégorisation s'appuie sur la caractérisation du contenu de la page numérisée à l'aide d'une analyse des descripteurs de texture, de forme, géométriques et topologiques. Cette caractérisation est définie à l'aide d'une représentation structurelle. Dans le détail, l'approche de catégorisation se décompose en deux étapes principales successives. La première consiste à extraire des régions homogènes. La seconde vise à proposer une signature structurelle à base de texture, sous la forme d'un graphe, construite à partir des régions homogènes extraites et reflétant la structure de la page analysée. Cette signature assure la mise en œuvre de nombreuses applications pour gérer efficacement un corpus ou des collections de livres patrimoniaux (par exemple, la recherche d'information dans les bibliothèques numériques en fonction de plusieurs critères, ou la catégorisation des pages d'un même ouvrage). En comparant les différentes signatures structurelles par le biais de la distance d'édition entre graphes, les similitudes entre les pages d'un même ouvrage en termes de leurs mises en page et/ou contenus peuvent être déduites. Ainsi de suite, les pages ayant des mises en page et/ou contenus similaires peuvent être catégorisées, et un résumé/une table des matières de l'ouvrage analysé peut être alors généré automatiquement. Pour illustrer l'efficacité de la signature proposée, une étude expérimentale détaillée a été menée dans ce travail pour évaluer deux applications possibles de catégorisation de pages d'un même ouvrage, la classification non supervisée de pages et la segmentation de flux de pages d'un même ouvrage. En outre, les différentes étapes de l'approche proposée ont donné lieu à des évaluations par le biais d'expérimentations menées sur un large corpus de documents patrimoniaux

A practical vision system for the detection of moving objects

The main goal of this thesis is to review and offer robust and efficient algorithms for the detection (or the segmentation) of foreground objects in indoor and outdoor scenes using colour image sequences captured by a stationary camera. For this purpose, the block diagram of a simple vision system is offered in Chapter 2. First this block diagram gives the idea of a precise order of blocks and their tasks, which should be performed to detect moving foreground objects. Second, a check mark () on the top right corner of a block indicates that this thesis contains a review of the most recent algorithms and/or some relevant research about it. In many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. Background subtraction has been widely used for this purpose as the first step. In this work, a review of the efficiency of a number of important background subtraction and modelling algorithms, along with their major features, are presented. In addition, two background approaches are offered. The first approach is a Pixel-based technique whereas the second one works at object level. For each approach, three algorithms are presented. They are called Selective Update Using Non-Foreground Pixels of the Input Image , Selective Update Using Temporal Averaging and Selective Update Using Temporal Median , respectively in this thesis. The first approach has some deficiencies, which makes it incapable to produce a correct dynamic background. Three methods of the second approach use an invariant colour filter and a suitable motion tracking technique, which selectively exclude foreground objects (or blobs) from the background frames. The difference between the three algorithms of the second approach is in updating process of the background pixels. It is shown that the Selective Update Using Temporal Median method produces the correct background image for each input frame. Representing foreground regions using their boundaries is also an important task. Thus, an appropriate RLE contour tracing algorithm has been implemented for this purpose. However, after the thresholding process, the boundaries of foreground regions often have jagged appearances. Thus, foreground regions may not correctly be recognised reliably due to their corrupted boundaries. A very efficient boundary smoothing method based on the RLE data is proposed in Chapter 7. It just smoothes the external and internal boundaries of foreground objects and does not distort the silhouettes of foreground objects. As a result, it is very fast and does not blur the image. Finally, the goal of this thesis has been presenting simple, practical and efficient algorithms with little constraints which can run in real time

Techniques for document image processing in compressed domain

The main objective for image compression is usually considered the minimization of storage space. However, as the need to frequently access images increases, it is becoming more important for people to process the compressed representation directly. In this work, the techniques that can be applied directly and efficiently to digital information encoded by a given compression algorithm are investigated. Lossless compression schemes and information processing algorithms for binary document images and text data are two closely related areas bridged together by the fast processing of coded data. The compressed domains, which have been addressed in this work, i.e., the ITU fax standards and JBIG standard, are two major schemes used for document compression. Based on ITU Group IV, a modified coding scheme, MG4, which explores the 2-dimensional correlation between scan lines, is developed. From the viewpoints of compression efficiency and processing flexibility of image operations, the MG4 coding principle and its feature-preserving behavior in the compressed domain are investigated and examined. Two popular coding schemes in the area of bi-level image compression, run-length and Group IV, are studied and compared with MG4 in the three aspects of compression complexity, compression ratio, and feasibility of compressed-domain algorithms. In particular, for the operations of connected component extraction, skew detection, and rotation, MG4 shows a significant speed advantage over conventional algorithms. Some useful techniques for processing the JBIG encoded images directly in the compressed domain, or concurrently while they are being decoded, are proposed and generalized; In the second part of this work, the possibility of facilitating image processing in the wavelet transform domain is investigated. The textured images can be distinguished from each other by examining their wavelet transforms. The basic idea is that highly textured regions can be segmented using feature vectors extracted from high frequency bands based on the observation that textured images have large energies in both high and middle frequencies while images in which the grey level varies smoothly are heavily dominated by the low-frequency channels in the wavelet transform domain. As a result, a new method is developed and implemented to detect textures and abnormalities existing in document images by using polynomial wavelets. Segmentation experiments indicate that this approach is superior to other traditional methods in terms of memory space and processing time

ClearPhoto - augmented photography

The widespread use of mobile devices has made known to the general public new areas that were hitherto confined to specialized devices. In general, the smartphone came to give all users the ability to execute multiple tasks, and among them, take photographs using the integrated cameras. Although these devices are continuously receiving improved cameras, their manufacturers do not take advantage of their full potential, since the operating systems normally offer simple APIs and applications for shooting. Therefore, taking advantage of this environment for mobile devices, we find ourselves in the best scenario to develop applications that help the user obtaining a good result when shooting. In an attempt to provide a set of techniques and tools more applied to the task, this dissertation presents, as a contribution, a set of tools for mobile devices that provides information in real-time on the composition of the scene before capturing an image. Thus, the proposed solution gives support to a user while capturing a scene with a mobile device. The user will be able to receive multiple suggestions on the composition of the scene, which will be based on rules of photography or other useful tools for photographers. The tools include horizon detection and graphical visualization of the color palette presented on the scenario being photographed. These tools were evaluated regarding the mobile device implementation and how users assess their usefulness

Cognitive abstraction approach to sketch-based image retrieval

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 151-157).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.As digital media become more popular, corporations and individuals gather an increasingly large number of digital images. As a collection grows to more than a few hundred images, the need for search becomes crucial. This thesis is addressing the problem of retrieving from a small database a particular image previously seen by the user. This thesis combines current findings in cognitive science with the knowledge of previous image retrieval systems to present a novel approach to content based image retrieval and indexing. We focus on algorithms which abstract away information from images in the same terms that a viewer abstracts information from an image. The focus in Imagina is on the matching of regions, instead of the matching of global measures. Multiple representations, focusing on shape and color, are used for every region. The matches of individual regions are combined using a saliency metric that accounts for differences in the distributions of metrics. Region matching along with configuration determines the overall match between a query and an image.by Manolis Kamvysselis and Ovidiu Marina.S.B.and M.Eng

Computer vision reading on stickers and direct part marking on horticultural products : challenges and possible solutions

Traceability of products from production to the consumer has led to a technological advancement in product identification. There has been development from the use of traditional one-dimensional barcodes (EAN-13, Code 128, etc.) to 2D (two-dimensional) barcodes such as QR (Quick Response) and Data Matrix codes. Over the last two decades there has been an increased use of Radio Frequency Identification (RFID) and Direct Part Marking (DPM) using lasers for product identification in agriculture. However, in agriculture there are still considerable challenges to adopting barcodes, RFID and DPM technologies, unlike in industry where these technologies have been very successful. This study was divided into three main objectives. Firstly, determination of the effect of speed, dirt, moisture and bar width on barcode detection was carried out both in the laboratory and a flower producing company, Brandkamp GmbH. This study developed algorithms for automation and detection of Code 128 barcodes under rough production conditions. Secondly, investigations were carried out on the effect of low laser marking energy on barcode size, print growth, colour and contrast on decoding 2D Data Matrix codes printed directly on apples. Three different apple varieties (Golden Delicious, Kanzi and Red Jonaprince) were marked with various levels of energy and different barcode sizes. Image processing using Halcon 11.0.1 (MvTec) was used to evaluate the markings on the apples. Finally, the third objective was to evaluate both algorithms for 1D and 2D barcodes. According to the results, increasing the speed and angle of inclination of the barcode decreased barcode recognition. Also, increasing the dirt on the surface of the barcode resulted in decreasing the successful detection of those barcodes. However, there was 100% detection of the Code 128 barcode at the company’s production speed (0.15 m/s) with the proposed algorithm. Overall, the results from the company showed that the image-based system has a future prospect for automation in horticultural production systems. It overcomes the problem of using laser barcode readers. The results for apples showed that laser energy, barcode size, print growth, type of product, contrast between the markings and the colour of the products, the inertia of the laser system and the days of storage all singularly or in combination with each other influence the readability of laser Data Matrix codes and implementation on apples. There was poor detection of the Data Matrix code on Kanzi and Red Jonaprince due to the poor contrast between the markings on their skins. The proposed algorithm is currently working successfully on Golden Delicious with 100% detection for 10 days using energy 0.108 J mm-2 and a barcode size of 10 × 10 mm2. This shows that there is a future prospect of not only marking barcodes on apples but also on other agricultural products for real time production