this paper, such as the global distribution of radiative energy in the tree crowns, which affects the amount of light reaching the leaves and the local temperature of plant organs. The presented framework itself is also open to further research. To begin, the precise functional specification of the environment, implied by the design of the modeling framework, is suitable for a formal analysis of algorithms that capture various environmental processes. This analysis may highlight tradeoffs between time, memory, and communication complexity, and lead to programs matching the needs of the model to available system resources in an optimal manner. A deeper understanding of the spectrum of processes taking place in the environment may lead to the design of a mini-language for environment specification. Analogous to the language of L-systems for plant specification, this mini-language would simplify the modeling of various environments, relieving the modeler from the burden of low-level programming in a general-purpose language. Fleischer and Barr's work on the specification of environments supporting collisions and reaction-diffusion processes [20] is an inspiring step in this direction. Complexity issues are not limited to the environment, but also arise in plant models. They become particularly relevant as the scope of modeling increases from individual plants to groups of plants and, eventually, entire plant communities. This raises the problem of selecting the proper level of abstraction for designing plant models, including careful selection of physiological processes incorporated into the model and the spatial resolution of the resulting structures. The complexity of the modeling task can be also addressed at the level of system design, by assigning various components o...

that the displayed images will have authentic visual appearance. In recent years the emphasis in realistic image synthesis has begun to shift from the simulation of light in an environment to images that look as real as the physical environment they portray. In other words the computer image should be not only

In this paper we develop a computational model of visual adaptation for realistic image synthesis based on psychophysical experiments. The model captures the changes in threshold visibility, color appearance, visual acuity, and sensitivity over time that are caused by the visual system

Our goal is to develop physically based lighting models and perceptually based rendering procedures for computer graphics that will produce synthetic images that are visually and measurably indistinguishable from real-world images. Fidelity of the physical simulation is of primary concern. Our

this paper it is referred to as the radiosity method".

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, we demonstrate the benefits of this extension by presenting several different algorithms exploiting its functionality to achieve high quality, high performance solutions for a variety of different applications from scientific visualization and realistic image synthesis. We conclude that pixel

We introduce a new concept for accelerating realistic image synthesis algorithms. At the core of this procedure is a novel physical error metric that correctly predicts the perceptual threshold for detecting artifacts in scene features. Built into this metric is a computational model of the human

An accurate and e#cient model of human perception has been developed to control the placement of samples in a realistic image synthesis algorithm. Previous sampling techniques have sought to spread the error equally across the image plane. However, this approach neglects the fact

. In this paper, we demonstrate the benefits of this extension by presenting several different algorithms exploitingits functionality to achieve high quality, high performance solutions for a variety of different applications from scientific visualizationand realistic image synthesis. We conclude that pixel