Cognitive Information Processing

Contains reports on three research projects.Joint Services Electronics Program (Contract DAAB07-74-C-0630)National Science Foundation (Grant GK-33736X2

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

A Survey of Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage (CH) applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

Towards robust real-world historical handwriting recognition

In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data

Oriental fonts auto boldness.

by Lo I Fan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references.Chapter Chapter 1: --- IntroductionChapter 1.1 --- The Evolution of Fonts --- p.1Chapter 1.2 --- Bitmap Fonts --- p.2Chapter 1.3 --- Outline FontsChapter 1.3.1 --- Arc and Vector Form --- p.4Chapter 1.3.2 --- Spline Form --- p.4Chapter 1.3.3 --- Pros and Cons of Outline Fonts --- p.8Chapter 1.4 --- Examples of Outline FontsChapter 1.4.1 --- Adobe's PostScript --- p.9Chapter 1.4.2 --- Apple's and Microsoft TrueTypeChapter 1.4.2.1 --- Outline Representation --- p.10Chapter 1.4.2.2 --- Rasterisation --- p.12Chapter 1.4.2.3 --- Hinting --- p.13Chapter 1.5 --- Bold FontsChapter 1.5.1 --- Definition of Bold --- p.15Chapter 1.5.2 --- Definition of Auto B oldness --- p.16Chapter 1.5.3 --- Auto Boldness by Double Printing --- p.17Chapter 1.5.4 --- Auto Boldness by Multi-Master Technique --- p.18Chapter 1.6 --- Chinese FontsChapter 1.6.1 --- Chinese Character Sets --- p.19Chapter 1.6.2 --- The Subtleties of Chinese Fonts Auto Boldness --- p.21Chapter 1.7 --- Project Objective --- p.23Chapter 1.8 --- Goals --- p.23Chapter Chapter 2: --- Main Ideas of Chinese Font Auto BoldnessChapter 2.1 --- Prototype of Auto Boldness Driver --- p.24Chapter 2.2 --- Design Features of the Prototype Auto Boldness Driver --- p.25Chapter 2.3 --- Data Structure and Algorithm of Auto BoldnessChapter 2.3.1 --- Data Structure of TrueType Character Outline --- p.27Chapter 2.3.2 --- Algorithm of Auto Boldness --- p.28Chapter 2.3.3 --- Algorithm Description --- p.29Chapter 2.4 --- Component Font Auto Boldness --- p.35Chapter Chapter 3: --- Language of Auto BoldnessChapter 3.1 --- Enhancements of TrueType Engine to support Auto Boldness --- p.36Chapter 3.2 --- Symmetric Bold Instruction --- p.38Chapter 3.3 --- Rotate Bold Instruction --- p.47Chapter 3.4 --- Asymmetric B old Instruction --- p.50Chapter 3.5 --- Comparison of Bold Instructions --- p.54Chapter 3.6 --- Serif Accommodation Instruction --- p.55Chapter Chapter 4: --- Shape Parsing and Auto Bold Code GenerationChapter 4.1 --- Compilation Process and Auto Boldness --- p.62Chapter 4.2 --- Shape Lexical Analyzer --- p.64Chapter 4.3 --- Shape Token Attributes EvaluationChapter 4.3.1 --- line Token --- p.66Chapter 4.3.2 --- bezier2 Token --- p.67Chapter 4.3.3 --- sharp Token --- p.70Chapter 4.3.4 --- concave Token --- p.75Chapter 4.3.5 --- convex Token --- p.75Chapter 4.4 --- Scope of Shape Parsing --- p.76Chapter 4.5 --- Shape Parsing Mechanism --- p.77Chapter 4.6 --- Model Grammar RulesChapter 4.6.1 --- Grammar Rule Format --- p.81Chapter 4.6.2 --- Grammar Rule Item --- p.82Chapter 4.6.3 --- Grammar Rule Assignment --- p.83Chapter 4.6.4 --- Grammar Rule Condition --- p.83Chapter 4.7 --- Auto Boldness Code Generation --- p.84Chapter 4.8 --- Program Methodology of Prototype Auto Boldness Driver --- p.86Chapter Chapter 5: --- ConclusionsChapter 5.1 --- Work Achieved --- p.87Chapter 5.2 --- The Pros and Cons of Auto Boldness Algorithm --- p.88Chapter 5.3 --- Bold Quality Assessments --- p.91Chapter 5.3 --- Future Directions --- p.93ReferencesAppendix OneAppendix Tw

Data input for scientific visualization

Since the development of the modular visualization environment, the users of such gen­eral software have had to face the problems of file input Simply put, the range and complexity of different file formats has prevented the developers of visualization systems from creating an individual solution for every format. This has left a gap, where users are left to fend for themselves by either extending the system to their needs, or using a format capable of being described by one of the input tools offered by such systems. Neither of these options is particularly easy, and the use of field dependent terminology can hamper such efforts.This thesis proposes a model, architecture and methodology, for importing uncommon file formats and data into scientific visualization systems by way of interpretation. Using interpretation we are able to describe many file formats in a general manner, enabling further development of simple methods to aid users in solving their data input problems. The utility of these concepts is illustrated through the Interactive File Input Toolkit (IFIT), which allows users to solve their file input problems in a flexible manner. This tool is illustrated by a range of examples and test cases, and unlike other solutions it has the ability to discover as well as describe the content of a file. Finally, this thesis presents work towards an automatic method for determining a file’s input parameters

Caries detection in panoramic dental x-ray images

The detection of dentalcaries,in a preliminar stage are of most importance. There is a long history of dental caries. Over a million years ago, hominids such as Australopithecus suffered from cavities. Archaeological evidence shows that tooth decay is an ancient disease dating far into prehistory. Skulls dating from a million years ago through the Neolithic period show signs of caries. The increase of caries during the Neolithic period may be attributed to the increase of plant foods containing carbohydrates. The beginning of rice cultivation in South Asia is also believed to have caused an increase in caries. DentalCaries,alsoknownasdentaldecayortoothdecay,isdefinedasadisease of the hard tissues of the teeth caused by the action of microorganisms, found in plaque,onfermentablecarbohydrates(principallysugars). Attheindividuallevel, dental caries is a preventable disease. Given its dynamic nature the dental caries disease, once established, can be treated or reversed prior to significant cavitation taking place. There three types of dental caries [59], the first type is the Enamel Caries, that is preceded by the formation of a microbial dental plaque. Secondly the Dentinal Caries which begins with the natural spread of the process along the natural spread of great numbers of the dentinal tubules. Thirdly the Pulpal Caries that corresponds to the root caries or root surface caries. Primary diagnosis involves inspection of all visible tooth surfaces using a good light source, dental mirror and explorer. Dental radiographs (X-rays) may show dental caries before it is otherwise visible, particularly caries between the teeth. Large dental caries are often apparent to the naked eye, but smaller lesions can be difficult to identify. Visual and tactile inspection along with radiographs are employed frequently among dentists. At times, caries may be difficult to detect. Bacteriacanpenetratetheenameltoreachdentin,butthentheoutersurfacemaybe at first site intact. These caries, sometimes referred to as "hidden caries", in the preliminary stage X-ray are the only way to detect them, despite of the visual examinationofthetoothshowntheenamelintactorminimallyperforated. Without X-rays wouldn’t be possible to detect these problems until they had become severe and caused serious damage. [...

Processing Camera-captured Document Images: Geometric Rectification, Mosaicing, and Layout Structure Recognition

This dissertation explores three topics: 1) geometric rectification of cameracaptured document images, 2) camera-captured document mosaicing, and 3) layout structure recognition. The first two topics pertain to camera-based document image analysis, a new trend within the OCR community. Compared to typical scanners,cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. The third topic is related to the need for efficient metadata extraction methods, critical for managing digitized documents. The kernel of our geometric rectification framework is a novel method for estimating document shape from a single camera-captured image. Our method uses texture flows detected in printed text areas and is insensitive to occlusion. Classification of planar versus curved documents is done automatically. For planar pages, we obtain full metric rectification. For curved pages, we estimate a planar-strip approximation based on properties of developable surfaces. Our method can process any planar or smoothly curved document captured from an arbitrary position without requiring 3D data, metric data, or camera calibration. For the second topic, we design a novel registration method for document images, which produces good results in difficult situations including large displacements, severe projective distortion, small overlapping areas, and lack of distinguishable feature points. We implement a selective image composition method that outperforms conventional image blending methods in overlapping areas. It eliminates double images caused by mis-registration and preserves the sharpness in overlapping areas. We solve the third topic with a graph-based model matching framework. Layout structures are modeled by graphs, which integrate local and global features and are extensible to new features in the future. Our model can handle large variation within a class and subtle differences between classes. Through graph matching, the layout structure of a document is discovered. Our layout structure recognition technique accomplishes document classification and logical component labeling at the same time. Our model learning method enables a model to adapt to changes in classes over time