Trabajo Colaborativo 1

TRABAJO COLABORATIVO #1

COMPUTACION GRAFICA

ALUMNOS:

Jayson Andrés Leyton Sarmiento

Ingeniería de sistemas

Rosa Mireya Camelo

Tecnologia de sistemas

Wilson Alejandro Castañeda

Ingeniería de Sistemas

TUTOR:

Oscar Javier Abaunza Garcia

Universidad Nacional Abierta y a Distancia

Ingeniería De Sistemas

CEAD Facatativa / Gacheta

2012

INTRODUCCION

La definición de la profesión del diseñador gráfico es más bien reciente, en lo que refiere su preparación, su actividad y sus objetivos. Aunque no existe consenso acerca de la fecha exacta en la que nació el diseño gráfico, algunos lo datan durante el período de entreguerras. Otros entienden que comienza a identificarse como tal para finales del siglo XIX.

Puede argumentarse que comunicaciones gráficas con propósitos específicos tienen su origen en las pinturas rupestres del Paleolítico y en el nacimiento del lenguaje escrito en el tercer milenio a. de C. Pero las diferencias de métodos de trabajo, ciencias auxiliares y formación requerida son tales que no es posible identificar con claridad al diseñador gráfico actual con el hombre de la prehistoria, con el xilógrafo del siglo XV o con el litógrafo de 1890.

La diversidad de opiniones responde a que algunos consideran como producto del diseño gráfico a toda manifestación gráfica y otros solamente a aquellas que surgen como resultado de la aplicación de un modelo de producción industrial; es decir, aquellas manifestaciones visuales que han sido "proyectadas" contemplando necesidades de diversos tipos: productivas, simbólicas, ergonómicas, contextuales

Actividad 1: Cada estudiante debe realizar la búsqueda en internet o en la biblioteca virtual de un artículo o ensayo en inglés, original y traducido con mínimo tres (3) páginas, dicho documento debe presentar una aplicación o avance en torno a la computación gráfica (incluir fuentes documentales).

Solucion:

Advanced Modeling Techniques for Computer Graphics

DAVID S. EBERT

Computer Science and Electrical Engineering Department, University of Maryland ^ebert@cs.umbc.edu&

In the past thirty years, modeling techniques in computer graphics have evolved significantly as the field has matured and attempted to portray the complexities of nature. Polygonal models, patches, points, and lines are insufficient to represent the complexities of natural objects and intricate man-made objects in

a manageable and controllable fashion. Higher-level modeling techniques have been developed to provide an abstraction of the model, encode classes of objects, and allow high-level control and specification of the model. The goal of these advanced modeling techniques is to provide

a concise, efficient, flexible, and controllable mechanism for specifying and animating models of complex objects and natural phenomena. Most of these advanced modeling techniques can be considered procedural modeling techniques: code segments or algorithms are used to abstract and encode the details of the model instead of explicitly storing vast numbers of low-level primitives. The use of algorithms unburdens the modeler/animator of low-level control, provides great flexibility, and allows amplification of his efforts through parametric control: a few parameters to the model yield large amounts of geometric details (Smith [1984] called this “database amplification”). This survey examines several types of procedural techniques, including fractals, grammar-based models, volumetric procedural models, implicit surfaces,and particle systems.

FRACTALS

Fractals [Peitgen et al. 1992] have a precise mathematical definition, but in computer graphics their definition has been extended to refer generally to models with a large degree of self-similarity: subpieces of the object appear to be scaled down, possibly translated and rotated versions of the original object.

Along these lines, Musgrave [Ebert et al. 1994] define a fractal as “a geometrically complex object, the complexity of which arises through the repetition of form over some range of scale.” Many natural objects exhibit this characteristic, including mountains, coastlines, trees, plants (e.g., cauliflower), water,

and clouds. Fractals can generally be classified as deterministic or non-deterministic (also called random fractals), depending on whether they contain randomness. Random fractals have been used extensively in computer graphics to model natural objects, most notably terrain. Most fractal terrain-generation algorithms work through recursive subdivision and pseudorandom perturbation. An original surface is defined and divided equally into subparts. New vertices are added and pseudorandomly displaced from the original surface, with a displacement magnitude that decreases at each iteration as the frequency increases. Therefore, the first iteration gives the large peaks on the surface, and later subdivisions add small-scale detail. Only the parameters for controlling the random-number generator, the level of subdivision, and the “roughness” of the surface are needed to define an extremely complex terrain. Recent work in fractals has included the simulation of diffusion-limited aggregation

(DLA) models and the use of multi-frac- tals [Ebert et al. 1994], which allows different fractal dimensions (degrees of “roughness”) in the models to simulate natural terrain better.

GRAMMAR-BASED MODELS

Grammar-based models, primarily Lsystems [Prusinkiewicz and Lindenmayer 1990], also allow natural complexity to be specified with a few parameters. Grammar- based

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