1 Graphical modeling using L-systems.- 1.1 Rewriting systems.- 1.2 DOL-systems.- 1.3 Turtle interpretation of strings.- 1.4 Synthesis of DOL-systems.- 1.4.1 Edge rewriting.- 1.4.2 Node rewriting.- 1.4.3 Relationship between edge and node rewriting.- 1.5 Modeling in three dimensions.- 1.6 Branching structures.- 1.6.1 Axial trees.- 1.6.2 Tree OL-systems.- 1.6.3 Bracketed OL-systems.- 1.7 Stochastic L-systems.- 1.8 Context-sensitive L-systems.- 1.9 Growth functions.- 1.10 Parametric L-systems.- 1.10.1 Parametric OL-systems.- 1.10.2 Parametric 2L-systems.- 1.10.3 Turtle interpretation of parametric words.- 2 Modeling of trees.- 3 Developmental models of herbaceous plants.- 3.1 Levels of model specification.- 3.1.1 Partial L-systems.- 3.1.2 Control mechanisms in plants.- 3.1.3 Complete models.- 3.2 Branching patterns.- 3.3 Models of inflorescences.- 3.3.1 Monopodial inflorescences.- 3.3.2 Sympodial inflorescences.- 3.3.3 Polypodial inflorescences.- 3.3.4 Modified racemes.- 4 Phyllotaxis.- 4.1 The planar model.- 4.2 The cylindrical model.- 5 Models of plant organs.- 5.1 Predefined surfaces.- 5.2 Developmental surface models.- 5.3 Models of compound leaves.- 6 Animation of plant development.- 6.1 Timed DOL-systems.- 6.2 Selection of growth functions.- 6.2.1 Development of nonbranching filaments.- 6.2.2 Development of branching structures.- 7 Modeling of cellular layers.- 7.1 Map L-systems.- 7.2 Graphical interpretation of maps.- 7.3 Microsorium linguaeforme.- 7.4 Dryopteris thelypteris.- 7.5 Modeling spherical cell layers.- 7.6 Modeling 3D cellular structures.- 8 Fractal properties of plants.- 8.1 Symmetry and self-similarity.- 8.2 Plant models and iterated function systems.- Epilogue.- Appendix A Software environment for plant modeling.- A.1 A virtual laboratory in botany.- A.2 List of laboratory programs.- Appendix B About the figures.- Turtle interpretation of symbols.

/pdf/the-algorithmic-beauty-of-plants-4f9yunhil9.pdf

The Algorithmic Beauty of Plants

This paper examines the major types of spatial data models currently known and places these models in a comprehensive framework. This framework is used to provide clarification of how varying data models, as well as their inherent advantages and disadvantages, are interrelated. It also provides an insight into how these conflicting demands may be balanced in a more systematic and predictable manner for practical applications, and reveals directions for needed future research. On examine les principaux types de modeles de donnees spatiales actuels, et on les place dans un cadre global. Ce cadre est utilise pour eclaircir comment ces divers modeles spatiaux, de meme que leurs avantages et desavantages, sont interrelies. Le cadre laisse aussi voir comment ces demandes contradictoires peuvent etre equilibrees d'une facon plus systematique et previsible pour des applications pratiques, et revele les directions que doit prendre la recherche future.

A conceptual framework and comparison of spatial data models.

Color image quantization is a process of representing an image with a small number of well selected colors. In this article an algorithm for multidimensional data clustering (termed the variance-based algorithm), based on the criterion of minimization of the sum-of-squared error, is applied to the problem of reducing the number of colors used to represent a given color image. The suitability of the sum-of-squared error criterion for measuring the similarity between the original and quantized images is examined using a digitized image and a computer-generated image. The experimental results indicate that this error measure is basically consistent with the perceived quality of the quantized image. The performance of the variance-based algorithm is compared with that of other algorithms for color image quantization in terms of quantization images generated using the colors selected by the variance-based and the mediancut algorithms are also presented.

Variance‐based color image quantization for frame buffer display

A space-filling curve is a linear traversal of a discrete finite multidimensional space. In order for this traversal to be useful in many applications, the curve should preserve "locality". We quantify "locality" and bound the locality of multidimensional space-filling curves. Classic Hilbert space-filling curves come close to achieving optimal locality.

/pdf/on-the-metric-properties-of-discrete-space-filling-curves-cuq8bpefla.pdf

On the metric properties of discrete space-filling curves

This paper introduces a new digital halftoning technique that uses space filling curves to generate aperiodic patterns of clustered dots. This method allows the parameterization of the size of pixel clusters, which can vary in one pixel steps. The algorithm unifies, in this way, the dispersed and clustered-dot dithering techniques.

http://www.visgraf.impa.br/Data/RefBib/PS_PDF/a91a/s91-velho.pdf

Digital halftoning with space filling curves

A fractal curve first discussed by Giuseppe Peano has useful statistical properties in image processing. The curve can act as a transform between a line and n-dimensional space retaining some of the spatially associative properties of the space. Applications of the curve include multispectral image display, compression in the color and spa-tial domain, classification, and color display. The technique enables the display of high quality color images on a frame store capable of displaying only 256 colors simultaneously. A logically ordered and complete set of colors for false color work can be selected. An im-provement in compression by about 3X for color images can be ob-tained by using this method in conjunction with an existing spatial compression technique. A simple algorithm for constructing the curves is shown.

Manipulation and Presentation of Multidimensional Image Data Using the Peano Scan

A novel method for analysing colour space is described with reference to compression, data ordering and display. This technique uses a precalculated curve first described by Giuseppe Peano to traverse the space, allowing sensible compression in the colour domain. Techniques already available for single channel data processing can then be applied because of the ordering properties peculiar to this family of curves. The immediate use, for which the technique was developed, enables the presentation of high colour fidelity images on a frame store able to display only 256 colours simultaneously, providing the frame store has a flexible look-up table (LUT). Other applications for false colour display and compression are outlined. The technique reduces the entropy in a multi-channel picture and is suitable for preprocessing before other compression techniques.

R. J. Stevens

Papers

Manipulation and Presentation of Multidimensional Image Data Using the Peano Scan

Compressing, Ordering And Displaying Image Data