Giving eyes to ICT!, or How does a computer recognize a cow?

Het door Schouten en andere onderzoekers op het CWI ontwikkelde systeem berust op het beschrijven van beelden met behulp van fractale meetkunde. De menselijke waarneming blijkt mede daardoor zo efficiënt omdat zij sterk werkt met gelijkenissen. Het ligt dus voor de hand het te zoeken in wiskundige methoden die dat ook doen. Schouten heeft daarom beeldcodering met behulp van 'fractals' onderzocht. Fractals zijn zelfgelijkende meetkundige figuren, opgebouwd door herhaalde transformatie (iteratie) van een eenvoudig basispatroon, dat zich daardoor op steeds kleinere schalen vertakt. Op elk niveau van detaillering lijkt een fractal op zichzelf (Droste-effect). Met fractals kan men vrij eenvoudig bedrieglijk echte natuurvoorstellingen maken. Fractale beeldcodering gaat ervan uit dat het omgekeerde ook geldt: een beeld effectief opslaan in de vorm van de basispatronen van een klein aantal fractals, samen met het voorschrift hoe het oorspronkelijke beeld daaruit te reconstrueren. Het op het CWI in samenwerking met onderzoekers uit Leuven ontwikkelde systeem is mede gebaseerd op deze methode. ISBN 906196502

Biomorphs via Modified Iterations

The aim of this paper is to present some modifications of the biomorphs generation algorithm introduced by Pickover in 1986. A biomorph stands for biological morphologies. It is obtained by a modified Julia set generation algorithm. The biomorph algorithm can be used in the creation of diverse and complicated forms resembling invertebrate organisms. In this paper the modifications of the biomorph algorithm in two directions are proposed. The first one uses different types of iterations (Picard, Mann, Ishikawa). The second one uses a sequence of parameters instead of one fixed parameter used in the original biomorph algorithm. Biomorphs generated by the modified algorithm are essentially different in comparison to those obtained by the standard biomorph algorithm, i.e., the algorithm with Picard iteration and one fixed constant

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

Three-dimensional alpha shapes

Frequently, data in scientific computing is in its abstract form a finite point set in space, and it is sometimes useful or required to compute what one might call the ``shape'' of the set. For that purpose, this paper introduces the formal notion of the family of $\alpha$-shapes of a finite point set in \Real^3. Each shape is a well-defined polytope, derived from the Delaunay triangulation of the point set, with a parameter \alpha \in \Real controlling the desired level of detail. An algorithm is presented that constructs the entire family of shapes for a given set of size $n$ in time $O(n^2)$, worst case. A robust implementation of the algorithm is discussed and several applications in the area of scientific computing are mentioned.Comment: 32 page

Interactive evolutionary 3D fractal modeling.

Pang, Wenjun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 83-88).Abstracts in English and Chinese.ACKNOWLEDGEMENTS --- p.iiABSTRACT --- p.iv摘要 --- p.vCONTENTS --- p.viList of Tables --- p.viiiList of Figures --- p.ixChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Recent research work --- p.4Chapter 1.2 --- Objectives --- p.8Chapter 1.3 --- Thesis Organization --- p.10Chapter 2. --- FRACTAL MODELING --- p.12Chapter 2.1 --- Fractal and Fractal Art --- p.12Chapter 2.2 --- Fractal Geometry --- p.15Chapter 2.3 --- Construction of Fractals --- p.21Chapter 2.4 --- Fractal Measurement and Aesthetics --- p.27Chapter 3. --- OVERVIEW OF EVOLUTIONARY DESIGN --- p.30Chapter 3.1 --- Initialization --- p.33Chapter 3.2 --- Selection --- p.33Chapter 3.3 --- Reproduction --- p.34Chapter 3.4 --- Termination --- p.36Chapter 4. --- EVOLUTIONARY 3D FRACTAL MODELING --- p.38Chapter 4.1 --- Fractal Construction --- p.38Chapter 4.1.1 --- Self-similar Condition of Fractal --- p.38Chapter 4.1.2 --- Fractal Transformation (FT) IFS Formulation --- p.39Chapter 4.1.3 --- IFS Genotype and Phenotype Expression --- p.41Chapter 4.2 --- Evolutionary Algorithm --- p.43Chapter 4.2.1 --- Single-point Crossover --- p.45Chapter 4.2.2 --- Arithmetic Gaussian mutation --- p.45Chapter 4.2.3 --- Inferior Elimination --- p.46Chapter 4.3 --- Interactive Fine-tuning using FT IFS --- p.46Chapter 4.4 --- Gaussian Fitness Function --- p.48Chapter 5. --- GAUSSIAN AESTHETIC FITNESS FUNCTION --- p.49Chapter 5.1 --- Fitness Considerations --- p.50Chapter 5.2 --- Fitness Function Formulation --- p.53Chapter 5.3 --- Results and Discussion on Fitness Function --- p.55Chapter 6. --- EXPERIMENT RESULTS and DISCUSSION --- p.59Chapter 6.1 --- Experiment of Evolutionary Generation --- p.59Chapter 6.2 --- Comparison on Different Methods --- p.60Chapter 7. --- 3D FRACTALS RENDERING and APPLICATION --- p.62Chapter 7.1 --- Transforming Property and User Modification --- p.62Chapter 7.2 --- Visualization and Rendering of 3D Fractals --- p.66Chapter 7.3 --- Applications in Design --- p.74Chapter 8. --- CONCLUSIONS and FUTURE WORK --- p.81Chapter 8.1 --- Conclusions --- p.81Chapter 8.2 --- Future Work --- p.81BIBLIOGRAPHY --- p.83Appendix --- p.89Marching Cubes Method --- p.8

Non-Standard Sound Synthesis with Dynamic Models

Full version unavailable due to 3rd party copyright restrictions.This Thesis proposes three main objectives: (i) to provide the concept of a new generalized non-standard synthesis model that would provide the framework for incorporating other non-standard synthesis approaches; (ii) to explore dynamic sound modeling through the application of new non-standard synthesis techniques and procedures; and (iii) to experiment with dynamic sound synthesis for the creation of novel sound objects. In order to achieve these objectives, this Thesis introduces a new paradigm for non-standard synthesis that is based in the algorithmic assemblage of minute wave segments to form sound waveforms. This paradigm is called Extended Waveform Segment Synthesis (EWSS) and incorporates a hierarchy of algorithmic models for the generation of microsound structures. The concepts of EWSS are illustrated with the development and presentation of a novel non-standard synthesis system, the Dynamic Waveform Segment Synthesis (DWSS). DWSS features and combines a variety of algorithmic models for direct synthesis generation: list generation and permutation, tendency masks, trigonometric functions, stochastic functions, chaotic functions and grammars. The core mechanism of DWSS is based in an extended application of Cellular Automata. The potential of the synthetic capabilities of DWSS is explored in a series of Case Studies where a number of sound object were generated revealing (i) the capabilities of the system to generate sound morphologies belonging to other non-standard synthesis approaches and, (ii) the capabilities of the system of generating novel sound objects with dynamic morphologies. The introduction of EWSS and DWSS is preceded by an extensive and critical overview on the concepts of microsound synthesis, algorithmic composition, the two cultures of computer music, the heretical approach in composition, non- standard synthesis and sonic emergence along with the thorough examination of algorithmic models and their application in sound synthesis and electroacoustic composition. This Thesis also proposes (i) a new definition for “algorithmic composition”, (ii) the term “totalistic algorithmic composition”, and (iii) four discrete aspects of non-standard synthesis

Iterated function systems and shape representation

We propose the use of iterated function systems as an isomorphic shape representation scheme for use in a machine vision environment. A concise description of the basic theory and salient characteristics of iterated function systems is presented and from this we develop a formal framework within which to embed a representation scheme. Concentrating on the problem of obtaining automatically generated two-dimensional encodings we describe implementations of two solutions. The first is based on a deterministic algorithm and makes simplifying assumptions which limit its range of applicability. The second employs a novel formulation of a genetic algorithm and is intended to function with general data input. Keywords: Machine Vision, Shape Representation, Iterated Function Systems, Genetic Algorithms