Reverse engineering of printed circuit boards: A conceptual idea

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB).The recent rapid growth in electronics devices, results escalating in the production number of the PCBs.For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products.This development can lead to waste and inefficiency when perfectly serviceable electronic components and appliances have to be scrapped because of the unavailability of spare PCB's from the Original Equipment Manufacturer (OEM) or are already obsolete.This paper proposed a novel framework for reverse engineering of obsolete single layer PCB.Equivalent PCB's which can be used as spares will be reproduced utilizing this new framework.This framework involves several steps, such as Data Acquisition, Image Processing, CAD Editing, PCB Fabrication and Circuitry testing and Analysis.Each stage of the framework and the functionality evaluation of the reproduced PCB will be discussed in detail in following sections

Raster to vector conversion: creating an unique handprint each time

When a person composes a document by hand, there is random variability in what is produced. That is, every letter is different from all others. If the person produces seven a s, none will be the same. This is not true when a computer prints something. When the computer produces seven a s they are all exactly the same. However, even with the variability inherent in a person s handwriting, when two people write something and they are compared side by side, they often appear as different as fonts from two computer families. In fact, if the two were intermixed to produce some text that has characters from each hand, it would not look right! The goal of this application is to improve the ability to digitally create testing materials (i. e., data collection documents) that give the appearance of being filled out manually (that is, by a person). We developed a set of capabilities that allow us to generate digital test decks using a raster database of handprinted characters, organized into hands (a single person s handprint). We wish to expand these capabilities using vector characters. The raster database has much utility to produce digital test deck materials. Vector characters, it is hoped, will allow greater control to morph the digital test data, within certain constraints. The long-term goal is to have a valid set of computer-generated hands that is virtually indistinguishable from characters created by a person

General Vectorization of Line Objects in Drawn Maps

Drawn maps consist of multiple object types. The most important are line objects which represent infrastructure. Attributes of these objects are essential for many tasks but in raster format they provide only low level information. Vectorization must be used to obtain vector data. In this paper general vectorization process consisting of five stages is proposed. For these stages short discussion and basic recommendations are given and some proper methods are presented

Adaptive image vectorisation and brushing using mesh colours

We propose the use of curved triangles and mesh colours as a vector primitive for image vectorisation. We show that our representation has clear benefits for rendering performance, texture detail, as well as further editing of the resulting vector images. The proposed method focuses on efficiency, but it still leads to results that compare favourably with those from previous work. We show results over a variety of input images ranging from photos, drawings, paintings, all the way to designs and cartoons. We implemented several editing workflows facilitated by our representation: interactive user-guided vectorisation, and novel raster-style feature-aware brushing capabilities

Rasterization techniques for Chinese outline fonts.

Kwong-ho Wu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 72-75).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Outline Fonts --- p.2Chapter 1.1.1 --- Advantages and Disadvantages --- p.4Chapter 1.1.2 --- Representations --- p.4Chapter 1.1.3 --- Rasterization --- p.5Chapter 1.2 --- Introduction to This Thesis --- p.6Chapter 1.2.2 --- Organization --- p.7Chapter 1.2.1 --- Objectives --- p.7Chapter 2 --- Chinese Characters Fonts --- p.8Chapter 2.1 --- Large Character Set --- p.8Chapter 2.2 --- Font Styles --- p.8Chapter 2.3 --- Storage Problems --- p.9Chapter 2.4 --- Hierarchical Structure --- p.10Chapter 2.5 --- High Stroke Count --- p.11Chapter 3 --- Rasterization --- p.13Chapter 3.1 --- The Basic Rasterization --- p.13Chapter 3.1.1 --- Scan Conversion --- p.14Chapter 3.1.2 --- Filling Outline --- p.16Chapter 3.2 --- Font Rasterization --- p.17Chapter 3.2.1 --- Outline Scaling --- p.17Chapter 3.2.2 --- Hintings --- p.17Chapter 3.2.3 --- Basic Rasterization Approach for Chinese Fonts --- p.18Chapter 3.3 --- Hintings --- p.20Chapter 3.3.1 --- Phase Control --- p.20Chapter 3.3.2 --- Auto-Hints --- p.21Chapter 3.3.3 --- Storage of Hintings Information in TrueType Font and Postscript Font --- p.22Chapter 4 --- An Improved Chinese Font Rasterizer --- p.24Chapter 4.1 --- Floating Point Avoidance --- p.24Chapter 4.2 --- Filling --- p.25Chapter 4.2.1 --- Filling with Horizontal Scan Line --- p.25Chapter 4.2.2 --- Filling with Vertical Scan Line --- p.27Chapter 4.3 --- Hintings --- p.30Chapter 4.3.1 --- Assumptions --- p.30Chapter 4.3.2 --- Maintaining Regular Strokes Width --- p.30Chapter 4.3.3 --- Maintaining Regular Spacing Among Strokes --- p.34Chapter 4.3.4 --- Hintings of Single Stroke Contour --- p.42Chapter 4.3.5 --- Storing the Hinting Information in Font File --- p.49Chapter 4.4 --- A Rasterization Algorithm for Printing --- p.51Chapter 4.4.1 --- A Simple Algorithm for Generating Smooth Characters --- p.52Chapter 4.4.2 --- Algorithm --- p.54Chapter 4.4.3 --- Results --- p.54Chapter 5 --- Experiments --- p.56Chapter 5.1 --- Apparatus --- p.56Chapter 5.2 --- Experiments for Investigating Rasterization Speed --- p.56Chapter 5.2.1 --- Investigation into the Effects of Features of Chinese Fonts on Rasterization Time --- p.56Chapter 5.2.2 --- Improvement of Fast Rasterizer --- p.57Chapter 5.2.3 --- Details of Experiments --- p.57Chapter 5.3 --- Experiments for Rasterization Speed of Font File with Hints --- p.57Chapter 6 --- Results and Conclusions --- p.58Chapter 6.1 --- Observations --- p.58Chapter 6.1.1 --- Relationship Between Time for Rasterization and Stroke Count --- p.58Chapter 6.1.2 --- Effects of Style --- p.61Chapter 6.1.3 --- Investigation into the Observed Relationship --- p.62Chapter 6.2 --- Improvement of the Improved Rasterizer --- p.64Chapter 6.3 --- Gain and Cost of Inserting Hints into Font File --- p.68Chapter 6.3.1 --- Cost --- p.68Chapter 6.3.2 --- Gain --- p.68Chapter 6.4 --- Conclusions --- p.69Chapter 6.5 --- Future Work --- p.69Appendi

Computerising 2D Animation and the Cleanup Power of Snakes

Traditional 2D animation remains largely a hand drawn process. Computer-assisted animation systems do exists. Unfortunately the overheads these systems incur have prevented them from being introduced into the traditional studio. One such problem area involves the transferral of the animator's line drawings into the computer system. The systems, which are presently available, require the images to be over-cleaned prior to scanning. The resulting raster images are of unacceptable quality. Therefore the question this thesis examines is; given a sketchy raster image is it possible to extract a cleaned-up vector image? Current solutions fail to extract the true line from the sketch because they possess no knowledge of the problem area. However, snakes use prior knowledge about the nature of sketchy images to deter-mine the correct line from several possibilities. As a snake is an energy minimising spline, the result is in vector format. Therefore in extracting the clean line from the sketch the conversion from raster to vector is also achieved. This technique makes snakes less prone to errors than the current algorithms. In reducing the errors, the overhead produced when transferring the drawings from paper to computer is also reduced

Automatic Retrieval of Skeletal Structures of Trees from Terrestrial Laser Scanner Data

Research on forest ecosystems receives high attention, especially nowadays with regard to sustainable management of renewable resources and the climate change. In particular, accurate information on the 3D structure of a tree is important for forest science and bioclimatology, but also in the scope of commercial applications. Conventional methods to measure geometric plant features are labor- and time-intensive. For detailed analysis, trees have to be cut down, which is often undesirable. Here, Terrestrial Laser Scanning (TLS) provides a particularly attractive tool because of its contactless measurement technique. The object geometry is reproduced as a 3D point cloud. The objective of this thesis is the automatic retrieval of the spatial structure of trees from TLS data. We focus on forest scenes with comparably high stand density and with many occlusions resulting from it. The varying level of detail of TLS data poses a big challenge. We present two fully automatic methods to obtain skeletal structures from scanned trees that have complementary properties. First, we explain a method that retrieves the entire tree skeleton from 3D data of co-registered scans. The branching structure is obtained from a voxel space representation by searching paths from branch tips to the trunk. The trunk is determined in advance from the 3D points. The skeleton of a tree is generated as a 3D line graph. Besides 3D coordinates and range, a scan provides 2D indices from the intensity image for each measurement. This is exploited in the second method that processes individual scans. Furthermore, we introduce a novel concept to manage TLS data that facilitated the researchwork. Initially, the range image is segmented into connected components. We describe a procedure to retrieve the boundary of a component that is capable of tracing inner depth discontinuities. A 2D skeleton is generated from the boundary information and used to decompose the component into sub components. A Principal Curve is computed from the 3D point set that is associated with a sub component. The skeletal structure of a connected component is summarized as a set of polylines. Objective evaluation of the results remains an open problem because the task itself is ill-defined: There exists no clear definition of what the true skeleton should be w.r.t. a given point set. Consequently, we are not able to assess the correctness of the methods quantitatively, but have to rely on visual assessment of results and provide a thorough discussion of the particularities of both methods. We present experiment results of both methods. The first method efficiently retrieves full skeletons of trees, which approximate the branching structure. The level of detail is mainly governed by the voxel space and therefore, smaller branches are reproduced inadequately. The second method retrieves partial skeletons of a tree with high reproduction accuracy. The method is sensitive to noise in the boundary, but the results are very promising. There are plenty of possibilities to enhance the method’s robustness. The combination of the strengths of both presented methods needs to be investigated further and may lead to a robust way to obtain complete tree skeletons from TLS data automatically.Die Erforschung des ÖkosystemsWald spielt gerade heutzutage im Hinblick auf den nachhaltigen Umgang mit nachwachsenden Rohstoffen und den Klimawandel eine große Rolle. Insbesondere die exakte Beschreibung der dreidimensionalen Struktur eines Baumes ist wichtig für die Forstwissenschaften und Bioklimatologie, aber auch im Rahmen kommerzieller Anwendungen. Die konventionellen Methoden um geometrische Pflanzenmerkmale zu messen sind arbeitsintensiv und zeitaufwändig. Für eine genaue Analyse müssen Bäume gefällt werden, was oft unerwünscht ist. Hierbei bietet sich das Terrestrische Laserscanning (TLS) als besonders attraktives Werkzeug aufgrund seines kontaktlosen Messprinzips an. Die Objektgeometrie wird als 3D-Punktwolke wiedergegeben. Basierend darauf ist das Ziel der Arbeit die automatische Bestimmung der räumlichen Baumstruktur aus TLS-Daten. Der Fokus liegt dabei auf Waldszenen mit vergleichsweise hoher Bestandesdichte und mit zahlreichen daraus resultierenden Verdeckungen. Die Auswertung dieser TLS-Daten, die einen unterschiedlichen Grad an Detailreichtum aufweisen, stellt eine große Herausforderung dar. Zwei vollautomatische Methoden zur Generierung von Skelettstrukturen von gescannten Bäumen, welche komplementäre Eigenschaften besitzen, werden vorgestellt. Bei der ersten Methode wird das Gesamtskelett eines Baumes aus 3D-Daten von registrierten Scans bestimmt. Die Aststruktur wird von einer Voxelraum-Repräsentation abgeleitet indem Pfade von Astspitzen zum Stamm gesucht werden. Der Stamm wird im Voraus aus den 3D-Punkten rekonstruiert. Das Baumskelett wird als 3D-Liniengraph erzeugt. Für jeden gemessenen Punkt stellt ein Scan neben 3D-Koordinaten und Distanzwerten auch 2D-Indizes zur Verfügung, die sich aus dem Intensitätsbild ergeben. Bei der zweiten Methode, die auf Einzelscans arbeitet, wird dies ausgenutzt. Außerdem wird ein neuartiges Konzept zum Management von TLS-Daten beschrieben, welches die Forschungsarbeit erleichtert hat. Zunächst wird das Tiefenbild in Komponenten aufgeteilt. Es wird eine Prozedur zur Bestimmung von Komponentenkonturen vorgestellt, die in der Lage ist innere Tiefendiskontinuitäten zu verfolgen. Von der Konturinformation wird ein 2D-Skelett generiert, welches benutzt wird um die Komponente in Teilkomponenten zu zerlegen. Von der 3D-Punktmenge, die mit einer Teilkomponente assoziiert ist, wird eine Principal Curve berechnet. Die Skelettstruktur einer Komponente im Tiefenbild wird als Menge von Polylinien zusammengefasst. Die objektive Evaluation der Resultate stellt weiterhin ein ungelöstes Problem dar, weil die Aufgabe selbst nicht klar erfassbar ist: Es existiert keine eindeutige Definition davon was das wahre Skelett in Bezug auf eine gegebene Punktmenge sein sollte. Die Korrektheit der Methoden kann daher nicht quantitativ beschrieben werden. Aus diesem Grund, können die Ergebnisse nur visuell beurteiltwerden. Weiterhinwerden die Charakteristiken beider Methoden eingehend diskutiert. Es werden Experimentresultate beider Methoden vorgestellt. Die erste Methode bestimmt effizient das Skelett eines Baumes, welches die Aststruktur approximiert. Der Detaillierungsgrad wird hauptsächlich durch den Voxelraum bestimmt, weshalb kleinere Äste nicht angemessen reproduziert werden. Die zweite Methode rekonstruiert Teilskelette eines Baums mit hoher Detailtreue. Die Methode reagiert sensibel auf Rauschen in der Kontur, dennoch sind die Ergebnisse vielversprechend. Es gibt eine Vielzahl von Möglichkeiten die Robustheit der Methode zu verbessern. Die Kombination der Stärken von beiden präsentierten Methoden sollte weiter untersucht werden und kann zu einem robusteren Ansatz führen um vollständige Baumskelette automatisch aus TLS-Daten zu generieren

3D silhouette rendering algorithms using vectorisation technique from Kedah topography map

Most of the applications in real world today are more toward non-photorealistic rather than photorealistic. Slihouette Rendering Algorithms is one of the technique that important in creating non-photorealistic image. It has been successful used in various applications such as game engine, communicating shape, cartoon rendering and 3D terrain visualization. This paper explores how silhouette rendering algorithms could be created using a data that have been extracted from Kedah topography map. Contour data from a topography map are convert from raster to vector (vectorisation) in order to create a grid terrain Digital Elevation Model (DEM) data. The vectorisation software has been used for producing these data. The data then convert into the format that suitable for existing 3D silhouette software. The results produced compatible terrain images of Sik District, Kedah that are closer to human drawn illustration and look like an artistic style

A methodology for constructing compact Chinese font libraries by radical composition.

by Wai-Yip Tung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves 55-56).Chapter 1. --- Introduction --- p.1Chapter 1.1. --- Previous work --- p.2Chapter 1.1.1. --- A Chinese METAFONT --- p.2Chapter 1.1.2. --- Chinese character generator --- p.2Chapter 1.1.3. --- Chinese Character Design System CCDS --- p.2Chapter 1.2. --- Goals of the thesis --- p.3Chapter 1.3. --- Overview of the thesis --- p.3Chapter 2. --- Construction of Chinese Characters --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2. --- liu shu(六書）Six Principles of Chinese Character Construction --- p.5Chapter 2.3. --- Structural Analysis of Chinese Characters --- p.7Chapter 2.3.1. --- Left-Right Structure --- p.8Chapter 2.3.2. --- Top-Bottom Structure --- p.9Chapter 2.3.3. --- Inside-Outside Structure --- p.10Chapter 2.3.4. --- Singleton Structure --- p.10Chapter 2.4. --- Usage frequency of radicals --- p.11Chapter 2.5. --- Usage frequency of Bushou --- p.11Chapter 2.6. --- Usage frequency of Shengpang --- p.13Chapter 2.7. --- Summary --- p.15Chapter 3. --- Composition by Radicals --- p.17Chapter 3.1. --- Introduction --- p.17Chapter 3.2. --- Transforming radicals --- p.18Chapter 3.3. --- Quality of transformed radicals --- p.19Chapter 3.4. --- Lower level components --- p.20Chapter 3.5. --- Summary --- p.23Chapter 4. --- Automatic Hinting for Chinese Font --- p.24Chapter 4.1 --- Introduction --- p.24Chapter 4.2. --- Automatic hinting for Chinese font --- p.26Chapter 4.3. --- Stroke recognition --- p.30Chapter 4.3.1. --- Identify horizontal lines --- p.31Chapter 4.3.2. --- Identify stroke segments --- p.31Chapter 4.3.3. --- Stroke recognition --- p.32Chapter 4.4. --- Regularize stroke width --- p.33Chapter 4.5. --- Grid-fitting horizontal and vertical strokes --- p.33Chapter 4.6. --- Grid-fitting radicals --- p.37Chapter 4.7. --- Summary --- p.39Chapter 5. --- RADIT - A Chinese Font Editor --- p.41Chapter 5.1. --- Introduction --- p.41Chapter 5.2. --- RADIT basics --- p.41Chapter 5.2.1. --- Character selection window --- p.42Chapter 5.2.2. --- Character window --- p.42Chapter 5.2.3. --- Tools Palette --- p.43Chapter 5.2.4. --- Toolbar --- p.43Chapter 5.2.5. --- Zooming the character window --- p.44Chapter 5.3. --- Editing a character --- p.44Chapter 5.3.1. --- Selecting handles --- p.44Chapter 5.3.2. --- Adding lines and curves --- p.45Chapter 5.3.3. --- Delete control points --- p.45Chapter 5.3.4. --- Moving control points --- p.45Chapter 5.3.5. --- Cut and paste --- p.46Chapter 5.3.6. --- Undo --- p.46Chapter 5.4. --- Adding radicals to a character --- p.46Chapter 5.5. --- Rasterizing and grid-fitting a character --- p.47Chapter 5.5.1. --- Rasterizing a character --- p.48Chapter 5.5.2. --- Stroke detection and regularization --- p.48Chapter 5.5.3. --- Grid-fitting and rasterizing a character --- p.49Chapter 6. --- Conclusions --- p.50Appendix A: Sample Fonts --- p.52References --- p.5