Case Studies on Fractal and Topological Analyses of Geographic Features Regarding Scale Issues
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Using Fractal Dimension for Target Detection in Clutter
KIM T. CONSTANTIKES USING FRACTAL DIMENSION FOR TARGET DETECTION IN CLUTTER The detection of targets in natural backgrounds requires that we be able to compute some characteristic of target that is distinct from background clutter. We assume that natural objects are fractals and that the irregularity or roughness of the natural objects can be characterized with fractal dimension estimates. Since man-made objects such as aircraft or ships are comparatively regular and smooth in shape, fractal dimension estimates may be used to distinguish natural from man-made objects. INTRODUCTION Image processing associated with weapons systems is fractal. Falconer1 defines fractals as objects with some or often concerned with methods to distinguish natural ob all of the following properties: fine structure (i.e., detail jects from man-made objects. Infrared seekers in clut on arbitrarily small scales) too irregular to be described tered environments need to distinguish the clutter of with Euclidean geometry; self-similar structure, with clouds or solar sea glint from the signature of the intend fractal dimension greater than its topological dimension; ed target of the weapon. The discrimination of target and recursively defined. This definition extends fractal from clutter falls into a category of methods generally into a more physical and intuitive domain than the orig called segmentation, which derives localized parameters inal Mandelbrot definition whereby a fractal was a set (e.g.,texture) from the observed image intensity in order whose "Hausdorff-Besicovitch dimension strictly exceeds to discriminate objects from background. Essentially, one its topological dimension.,,2 The fine, irregular, and self wants these parameters to be insensitive, or invariant, to similar structure of fractals can be experienced firsthand the kinds of variation that the objects and background by looking at the Mandelbrot set at several locations and might naturally undergo because of changes in how they magnifications. -
The Fractal Dimension of Islamic and Persian Four-Folding Gardens
Edinburgh Research Explorer The fractal dimension of Islamic and Persian four-folding gardens Citation for published version: Patuano, A & Lima, MF 2021, 'The fractal dimension of Islamic and Persian four-folding gardens', Humanities and Social Sciences Communications, vol. 8, 86. https://doi.org/10.1057/s41599-021-00766-1 Digital Object Identifier (DOI): 10.1057/s41599-021-00766-1 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Humanities and Social Sciences Communications General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 06. Oct. 2021 ARTICLE https://doi.org/10.1057/s41599-021-00766-1 OPEN The fractal dimension of Islamic and Persian four- folding gardens ✉ Agnès Patuano 1 & M. Francisca Lima 2 Since Benoit Mandelbrot (1924–2010) coined the term “fractal” in 1975, mathematical the- ories of fractal geometry have deeply influenced the fields of landscape perception, archi- tecture, and technology. Indeed, their ability to describe complex forms nested within each fi 1234567890():,; other, and repeated towards in nity, has allowed the modeling of chaotic phenomena such as weather patterns or plant growth. -
The Implications of Fractal Fluency for Biophilic Architecture
JBU Manuscript TEMPLATE The Implications of Fractal Fluency for Biophilic Architecture a b b a c b R.P. Taylor, A.W. Juliani, A. J. Bies, C. Boydston, B. Spehar and M.E. Sereno aDepartment of Physics, University of Oregon, Eugene, OR 97403, USA bDepartment of Psychology, University of Oregon, Eugene, OR 97403, USA cSchool of Psychology, UNSW Australia, Sydney, NSW, 2052, Australia Abstract Fractals are prevalent throughout natural scenery. Examples include trees, clouds and coastlines. Their repetition of patterns at different size scales generates a rich visual complexity. Fractals with mid-range complexity are particularly prevalent. Consequently, the ‘fractal fluency’ model of the human visual system states that it has adapted to these mid-range fractals through exposure and can process their visual characteristics with relative ease. We first review examples of fractal art and architecture. Then we review fractal fluency and its optimization of observers’ capabilities, focusing on our recent experiments which have important practical consequences for architectural design. We describe how people can navigate easily through environments featuring mid-range fractals. Viewing these patterns also generates an aesthetic experience accompanied by a reduction in the observer’s physiological stress-levels. These two favorable responses to fractals can be exploited by incorporating these natural patterns into buildings, representing a highly practical example of biophilic design Keywords: Fractals, biophilia, architecture, stress-reduction, -
Measuring the Fractal Dimensions of Empirical Cartographic Curves
MEASURING THE FRACTAL DIMENSIONS OF EMPIRICAL CARTOGRAPHIC CURVES Mark C. Shelberg Cartographer, Techniques Office Aerospace Cartography Department Defense Mapping Agency Aerospace Center St. Louis, AFS, Missouri 63118 Harold Moellering Associate Professor Department of Geography Ohio State University Columbus, Ohio 43210 Nina Lam Assistant Professor Department of Geography Ohio State University Columbus, Ohio 43210 Abstract The fractal dimension of a curve is a measure of its geometric complexity and can be any non-integer value between 1 and 2 depending upon the curve's level of complexity. This paper discusses an algorithm, which simulates walking a pair of dividers along a curve, used to calculate the fractal dimensions of curves. It also discusses the choice of chord length and the number of solution steps used in computing fracticality. Results demonstrate the algorithm to be stable and that a curve's fractal dimension can be closely approximated. Potential applications for this technique include a new means for curvilinear data compression, description of planimetric feature boundary texture for improved realism in scene generation and possible two-dimensional extension for description of surface feature textures. INTRODUCTION The problem of describing the forms of curves has vexed researchers over the years. For example, a coastline is neither straight, nor circular, nor elliptic and therefore Euclidean lines cannot adquately describe most real world linear features. Imagine attempting to describe the boundaries of clouds or outlines of complicated coastlines in terms of classical geometry. An intriguing concept proposed by Mandelbrot (1967, 1977) is to use fractals to fill the void caused by the absence of suitable geometric representations. -
Fractals, Self-Similarity & Structures
© Landesmuseum für Kärnten; download www.landesmuseum.ktn.gv.at/wulfenia; www.biologiezentrum.at Wulfenia 9 (2002): 1–7 Mitteilungen des Kärntner Botanikzentrums Klagenfurt Fractals, self-similarity & structures Dmitry D. Sokoloff Summary: We present a critical discussion of a quite new mathematical theory, namely fractal geometry, to isolate its possible applications to plant morphology and plant systematics. In particular, fractal geometry deals with sets with ill-defined numbers of elements. We believe that this concept could be useful to describe biodiversity in some groups that have a complicated taxonomical structure. Zusammenfassung: In dieser Arbeit präsentieren wir eine kritische Diskussion einer völlig neuen mathematischen Theorie, der fraktalen Geometrie, um mögliche Anwendungen in der Pflanzen- morphologie und Planzensystematik aufzuzeigen. Fraktale Geometrie behandelt insbesondere Reihen mit ungenügend definierten Anzahlen von Elementen. Wir meinen, dass dieses Konzept in einigen Gruppen mit komplizierter taxonomischer Struktur zur Beschreibung der Biodiversität verwendbar ist. Keywords: mathematical theory, fractal geometry, self-similarity, plant morphology, plant systematics Critical editions of Dean Swift’s Gulliver’s Travels (see e.g. SWIFT 1926) recognize a precise scale invariance with a factor 12 between the world of Lilliputians, our world and that one of Brobdingnag’s giants. Swift sarcastically followed the development of contemporary science and possibly knew that even in the previous century GALILEO (1953) noted that the physical laws are not scale invariant. In fact, the mass of a body is proportional to L3, where L is the size of the body, whilst its skeletal rigidity is proportional to L2. Correspondingly, giant’s skeleton would be 122=144 times less rigid than that of a Lilliputian and would be destroyed by its own weight if L were large enough (cf. -
Harmonic and Fractal Image Analysis (2001), Pp. 3 - 5 3
O. Zmeskal et al. / HarFA - Harmonic and Fractal Image Analysis (2001), pp. 3 - 5 3 Fractal Analysis of Image Structures Oldřich Zmeškal, Michal Veselý, Martin Nežádal, Miroslav Buchníček Institute of Physical and Applied Chemistry, Brno University of Technology Purkynova 118, 612 00 Brno, Czech Republic [email protected] Introduction of terms Fractals − Fractals are of rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced copy of the whole. − They are crinkly objects that defy conventional measures, such as length and are most often characterised by their fractal dimension − They are mathematical sets with a high degree of geometrical complexity that can model many natural phenomena. Almost all natural objects can be observed as fractals (coastlines, trees, mountains, and clouds). − Their fractal dimension strictly exceeds topological dimension Fractal dimension − The number, very often non-integer, often the only one measure of fractals − It measures the degree of fractal boundary fragmentation or irregularity over multiple scales − It determines how fractal differs from Euclidean objects (point, line, plane, circle etc.) Monofractals / Multifractals − Just a small group of fractals have one certain fractal dimension, which is scale invariant. These fractals are monofractals − The most of natural fractals have different fractal dimensions depending on the scale. They are composed of many fractals with the different fractal dimension. They are called „multifractals“ − To characterise set of multifractals (e.g. set of the different coastlines) we do not have to establish all their fractal dimensions, it is enough to evaluate their fractal dimension at the same scale Self-similarity/ Semi-self similarity − Fractal is strictly self-similar if it can be expressed as a union of sets, each of which is an exactly reduced copy (is geometrically similar to) of the full set (Sierpinski triangle, Koch flake). -
Fractal Initialization for High-Quality Mapping with Self-Organizing Maps
Neural Comput & Applic DOI 10.1007/s00521-010-0413-5 ORIGINAL ARTICLE Fractal initialization for high-quality mapping with self-organizing maps Iren Valova • Derek Beaton • Alexandre Buer • Daniel MacLean Received: 15 July 2008 / Accepted: 4 June 2010 Ó Springer-Verlag London Limited 2010 Abstract Initialization of self-organizing maps is typi- 1.1 Biological foundations cally based on random vectors within the given input space. The implicit problem with random initialization is Progress in neurophysiology and the understanding of brain the overlap (entanglement) of connections between neu- mechanisms prompted an argument by Changeux [5], that rons. In this paper, we present a new method of initiali- man and his thought process can be reduced to the physics zation based on a set of self-similar curves known as and chemistry of the brain. One logical consequence is that Hilbert curves. Hilbert curves can be scaled in network size a replication of the functions of neurons in silicon would for the number of neurons based on a simple recursive allow for a replication of man’s intelligence. Artificial (fractal) technique, implicit in the properties of Hilbert neural networks (ANN) form a class of computation sys- curves. We have shown that when using Hilbert curve tems that were inspired by early simplified model of vector (HCV) initialization in both classical SOM algo- neurons. rithm and in a parallel-growing algorithm (ParaSOM), Neurons are the basic biological cells that make up the the neural network reaches better coverage and faster brain. They form highly interconnected communication organization. networks that are the seat of thought, memory, con- sciousness, and learning [4, 6, 15]. -
FRACTAL CURVES 1. Introduction “Hike Into a Forest and You Are Surrounded by Fractals. the In- Exhaustible Detail of the Livin
FRACTAL CURVES CHELLE RITZENTHALER Abstract. Fractal curves are employed in many different disci- plines to describe anything from the growth of a tree to measuring the length of a coastline. We define a fractal curve, and as a con- sequence a rectifiable curve. We explore two well known fractals: the Koch Snowflake and the space-filling Peano Curve. Addition- ally we describe a modified version of the Snowflake that is not a fractal itself. 1. Introduction \Hike into a forest and you are surrounded by fractals. The in- exhaustible detail of the living world (with its worlds within worlds) provides inspiration for photographers, painters, and seekers of spiri- tual solace; the rugged whorls of bark, the recurring branching of trees, the erratic path of a rabbit bursting from the underfoot into the brush, and the fractal pattern in the cacophonous call of peepers on a spring night." Figure 1. The Koch Snowflake, a fractal curve, taken to the 3rd iteration. 1 2 CHELLE RITZENTHALER In his book \Fractals," John Briggs gives a wonderful introduction to fractals as they are found in nature. Figure 1 shows the first three iterations of the Koch Snowflake. When the number of iterations ap- proaches infinity this figure becomes a fractal curve. It is named for its creator Helge von Koch (1904) and the interior is also known as the Koch Island. This is just one of thousands of fractal curves studied by mathematicians today. This project explores curves in the context of the definition of a fractal. In Section 3 we define what is meant when a curve is fractal. -
Analyse D'un Modèle De Représentations En N Dimensions De
Analyse d’un modèle de représentations en n dimensions de l’ensemble de Mandelbrot, exploration de formes fractales particulières en architecture. Jean-Baptiste Hardy 1 Département d’architecture de l’INSA Strasbourg ont contribués à la réalisation de ce mémoire Pierre Pellegrino Directeur de mémoire Emmanuelle P. Jeanneret Docteur en architecture Francis Le Guen Fractaliste, journaliste, explorateur Serge Salat Directeur du laboratoire des morphologies urbaines Novembre 2013 2 Sommaire Avant-propos .................................................................................................. 4 Introduction .................................................................................................... 5 1 Introduction aux fractales : .................................................... 6 1-1 Présentation des fractales �������������������������������������������������������������������� 6 1-1-1 Les fractales en architecture ������������������������������������������������������������� 7 1-1-2 Les fractales déterministes ................................................................ 8 1-2 Benoit Mandelbrot .................................................................................. 8 1-2-1 L’ensemble de Mandelbrot ................................................................ 8 1-2-2 Mandelbrot en architecture .............................................................. 9 Illustrations ................................................................................................... 10 2 Un modèle en n dimensions : -
Turtlefractalsl2 Old
Lecture 2: Fractals from Recursive Turtle Programs use not vain repetitions... Matthew 6: 7 1. Fractals Pictured in Figure 1 are four fractal curves. What makes these shapes different from most of the curves we encountered in the previous lecture is their amazing amount of fine detail. In fact, if we were to magnify a small region of a fractal curve, what we would typically see is the entire fractal in the large. In Lecture 1, we showed how to generate complex shapes like the rosette by applying iteration to repeat over and over again a simple sequence of turtle commands. Fractals, however, by their very nature cannot be generated simply by repeating even an arbitrarily complicated sequence of turtle commands. This observation is a consequence of the Looping Lemmas for Turtle Graphics. Sierpinski Triangle Fractal Swiss Flag Koch Snowflake C-Curve Figure 1: Four fractal curves. 2. The Looping Lemmas Two of the simplest, most basic turtle programs are the iterative procedures in Table 1 for generating polygons and spirals. The looping lemmas assert that all iterative turtle programs, no matter how many turtle commands appear inside the loop, generate shapes with the same general symmetries as these basic programs. (There is one caveat here, that the iterating index is not used inside the loop; otherwise any turtle program can be simulated by iteration.) POLY (Length, Angle) SPIRAL (Length, Angle, Scalefactor) Repeat Forever Repeat Forever FORWARD Length FORWARD Length TURN Angle TURN Angle RESIZE Scalefactor Table 1: Basic procedures for generating polygons and spirals via iteration. Circle Looping Lemma Any procedure that is a repetition of the same collection of FORWARD and TURN commands has the structure of POLY(Length, Angle), where Angle = Total Turtle Turning within the Loop Length = Distance from Turtle’s Initial Position to Turtle’s Final Position within the Loop That is, the two programs have the same boundedness, closing, and symmetry. -
Introduction to Fractal Geometry: Definition, Concept, and Applications
University of Northern Iowa UNI ScholarWorks Presidential Scholars Theses (1990 – 2006) Honors Program 1992 Introduction to fractal geometry: Definition, concept, and applications Mary Bond University of Northern Iowa Let us know how access to this document benefits ouy Copyright ©1992 - Mary Bond Follow this and additional works at: https://scholarworks.uni.edu/pst Part of the Geometry and Topology Commons Recommended Citation Bond, Mary, "Introduction to fractal geometry: Definition, concept, and applications" (1992). Presidential Scholars Theses (1990 – 2006). 42. https://scholarworks.uni.edu/pst/42 This Open Access Presidential Scholars Thesis is brought to you for free and open access by the Honors Program at UNI ScholarWorks. It has been accepted for inclusion in Presidential Scholars Theses (1990 – 2006) by an authorized administrator of UNI ScholarWorks. For more information, please contact [email protected]. INTRODUCTION TO FRACTAL GEOMETRY: DEFINITI0N7 CONCEPT 7 AND APP LI CA TIO NS MAY 1992 BY MARY BOND UNIVERSITY OF NORTHERN IOWA CEDAR F ALLS7 IOWA • . clouds are not spheres, mountains are not cones~ coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line, . • Benoit B. Mandelbrot For centuries, geometers have utilized Euclidean geometry to describe, measure, and study the world around them. The quote from Mandelbrot poses an interesting problem when one tries to apply Euclidean geometry to the natural world. In 1975, Mandelbrot coined the term ·tractal. • He had been studying several individual ·mathematical monsters.· At a young age, Mandelbrot had read famous papers by G. Julia and P . Fatou concerning some examples of these monsters. When Mandelbrot was a student at Polytechnique, Julia happened to be one of his teachers. -
Fractal Analysis of the Vascular Tree in the Human Retina
3 Jun 2004 22:39 AR AR220-BE06-17.tex AR220-BE06-17.sgm LaTeX2e(2002/01/18) P1: IKH 10.1146/annurev.bioeng.6.040803.140100 Annu. Rev. Biomed. Eng. 2004. 6:427–52 doi: 10.1146/annurev.bioeng.6.040803.140100 Copyright c 2004 by Annual Reviews. All rights reserved First published online as a Review in Advance on April 13, 2004 FRACTAL ANALYSIS OF THE VASCULAR TREE IN THE HUMAN RETINA BarryR.Masters Formerly, Gast Professor, Department of Ophthalmology, University of Bern, 3010 Bern, Switzerland; email: [email protected] Key Words fractals, fractal structures, eye, bronchial tree, retinal circulation, retinal blood vessel patterns, Murray Principle, optimal vascular tree, human lung, human bronchial tree I Abstract The retinal circulation of the normal human retinal vasculature is statis- tically self-similar and fractal. Studies from several groups present strong evidence that the fractal dimension of the blood vessels in the normal human retina is approximately 1.7. This is the same fractal dimension that is found for a diffusion-limited growth process, and it may have implications for the embryological development of the retinal vascular system. The methods of determining the fractal dimension for branching trees are reviewed together with proposed models for the optimal formation (Murray Princi- ple) of the branching vascular tree in the human retina and the branching pattern of the human bronchial tree. The limitations of fractal analysis of branching biological struc- tures are evaluated. Understanding the design principles of branching vascular systems and the human bronchial tree may find applications in tissue and organ engineering, i.e., bioartificial organs for both liver and kidney.