When observing an intense light source, humans perceive dense radiating spikes known as glare/starburst patterns. These patterns are frequently used in computer graphics applications to enhance the perception of brightness (e.g., in games and films). Previous works have computed the physical energy distribution of glare patterns under daytime conditions using approximations like Fresnel diffraction. These techniques are capable of producing visually believable results, particularly when the pupil remains small. However, they are insufficient under nighttime conditions, when the pupil is significantly dilated and the assumptions behind the approximations no longer hold. To address this, we employ the Rayleigh-Sommerfeld diffraction solution, from which Fresnel diffraction is derived as an approximation, as our baseline reference. In pursuit of performance and visual quality, we also employ Ochoa's approximation and the Chirp Z transform to efficiently generate high-resolution results for computer graphics applications. By also taking into account background illumination and certain physiological characteristics of the human photoreceptor cells, particularly the visual threshold of light stimulus, we propose a framework capable of producing plausible visual depictions of glare patterns for both daytime and nighttime scenes.

The perception of realism in computer-generated images can be significantly enhanced by subtle visual cues. Among those, one can highlight the presence of dust on synthetic objects, which is often subject to temporal variations in real settings. In this paper, we present a framework for the generation of textures representing the accumulation of this ubiquitous material over time in indoor settings. It employs a physically inspired approach to portray the effects of different levels of accumulated dust roughness on the appearance of substrate surfaces and to modulate these effects according to the different illumination and viewing geometries. The development of its core algorithms was guided by empirical insights and data obtained from observational experiments which are also described. To illustrate its applicability to the rendering of visually plausible depictions of time-dependent changes in dusty scenes, we provide sequences of images obtained considering distinct dust accumulation scenarios.

The identification and analysis of hyperspectral visible and near-infrared signatures of key targets, notably silica-rich outcrops, on Mars can be considerably affected by the presence of covering regolith materials. Before reaching and after being reflected by these targets, the impinging electromagnetic radiation is propagated through the regolith materials. Hence, the investigation of these materials' light transmission capabilities, the focal point of this work, can be instrumental for the correct interpretation of the hyperspectral responses of silica-rich deposits and other subsurface targets. In the absence of actual Martian regolith samples to be used in transmittance experiments, high-fidelity modeling approaches can effectively contribute to advance the knowledge in this area. Accordingly, we carried out a controlled assessment of the quantitative and qualitative traits of light transmitted through representative samples of Martian regolith using a first-principles simulation framework supported by radiometric data acquired in the Gusev crater on Mars. We also examined changes in the light transmission profiles of Martian regoliths caused by environmentally-induced variations in their grains' morphology.

The expansion of landscapes formed by sand-textured soils is increasing due to aridity and desertification processes elicited by human activities and climate change. Vegetation restoration initiatives are instrumental to mitigate this trend. These initiatives involve the combined use of satellite, ground-based and in silico data in the detection and management of abiotic stress factors affecting seed germination and plant development in these regions. These photobiological phenomena, in turn, are often mediated by the red to far-red ratios of impinging light. In this paper, we examine the key differences between the red to far-red ratios of light propagated by sand-textured soils characterized by either a dominant presence of hematite or goethite (limonite), the mineral impurities largely responsible for their colors. Moreover, we also address the sensitivity of these ratios to different water distribution patterns: present in these soils' pore space or forming films around their constituent grains. By strengthening the current understanding about the interconnected effects of these abiotic factors, our findings are expected to contribute to the development of new cost-effective technologies for the monitoring (in situ and remotely) of sandy landscapes and the restoration of vegetation in these regions.

The integration of remote sensing technologies, ground-based data and predictive modeling has been instrumental in the increase of agricultural output, particularly with respect to the large-scale cultivation of ubiquitous C3 species such as protein-rich soybean. Further advances can be pursued in this area, however, since key aspects directly associated with crop productivity still remain relatively unexplored. It has been recognized that variations in the red to far-red ratio of light propagated by a plant leaf can influence the growth and the photosynthetic efficiency of adjacent leaves, adjacent plants and, ultimately, affect the entire crop. Soybean plants, like other C3 species, are characterized by having bifacial leaves whose adaxial (face) and abaxial (back) surfaces depict distinct reflectance and transmittance profiles. In this work, we investigate the impact of these profiles on the red to far-red ratios of light propagated by these leaves, and discuss its implications for the optimization and monitoring of cultivation conditions. Our findings provide a comprehensive portrait of the angular and directional (downward and upward the plant canopy) variations of these ratios. Accordingly, they are expected to contribute to the elucidation of fundamental processes associated with the variation patterns of these ratios within cultivated fields. Such a knowledge is essential for the effective management of agricultural resources required for the cultivation of high-yield and healthy crops.

Crops formed by C4plant species (e.g., maize and sugarcane) continue to be in high demand for both food and biofuel production worldwide. In order to meet this demand while conserving agricultural resources, comprehensive scientific efforts need to be directed toward the reduction of crop yield losses, which are often associated with crop-weed competition. The germination of seeds from different weed species depends on the red to far-red ratio of light reaching them. In the case of seeds buried under C4 plants, this light may have been transmitted through these plants' unifacial leaves. These, in turn, may have been subjected to varying levels of nutrient stress, notably those resulting from a lack of nitrogen. In this work, we investigate the red to far-red ratios of light transmitted through nitrogen-deficient unifacial leaves. Our findings suggest that this type of stress can lead to a further surge in yield losses by increasing the red to far-red ratios of light transmitted through the leaves and, thus, contributing to an increase in weed seed germination. Accordingly, our findings are expected to further the current understanding about stress factors limiting C4 crop yields. Such an understanding is essential for the development of more effective technologies for the monitoring (remote and in situ) and management of these factors.

Reliable interpretations of remote sensing data obtained from Mars, notably in the visible and near-infrared spectral domains, need to correctly account for variations on the reflectance of the sand-textured regolith covering its surface. These variations, in turn, are directly associated not only with the presence of iron oxides, which are also found in dune fields and coastal landscapes on Earth, but also with the composition of the core (parent) material forming different types of Martian regolith. While the core materials of terrestrial sand-textured soils can be clearly identified, the same cannot be said about the core materials of Martian regolith. The difficulties to solve this open question are mainly prompted by the relatively restricted range of experiments that can be performed using equipment deployed on Mars. In fact, missions have been proposed to overcome these restrictions by collecting and bringing samples of Martian soils to Earth. In the meantime, the pairing of remote sensing technology with in silico experiments continues to play a key role in investigations of Martian geomorphology, mineralogy and weathering history. Following this trend, we have compared the effects of different types of core materials on the reflectance of Martian regolith in order to add to the current knowledge about its composition. The core material candidates were selected based on remote and in situ observations of terrestrial and Martian sand-textured soils. Our in silico experiments were performed using a first-principles simulation framework in conjunction with measured reflectance data obtained from different regions on Mars. Besides contributing to the elucidation of the problem at hand, our findings enable an original and predictive assessment of the impact of different core materials on the spectral signatures of terrestrial and non-terrestrial sand-textured soils.

Sand-textured soils are found in a wide range of landscapes, from coastal areas to dune fields. The quantification of light penetration through these soils is of considerable interest not only for remote sensing applications, but also for agricultural, ecological and geophysical studies. Despite its importance, however, the literature on this topic is still scarce. Moreover, the available light penetration (transmittance) datasets for these soils are affected by experimental and modeling limitations. These include, for instance, the use of samples with morphological and mineralogical characteristics markedly different from those of naturally occurring sand-textured soils. To overcome these limitations and strengthen the current knowledge about light penetration in these soils, commonly referred to as natural sands, we propose the use of a predictive in silico experimental framework supported by measured data. Our findings demonstrate that it is necessary to properly account for the varying particle distributions and the iron oxide contents of natural sands in order to obtain penetration depth estimations that can be reliably employed in investigations involving these soils. In addition, our in silico experiments allow for a diversified assessment of the light transmission profiles of natural sands at different depths with respect to spectral and angular dependencies.

Abstract The visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modelling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas.

Abstract Predictive light transport models based on first‐principles simulation approaches have been proposed for complex organic materials. The driving force behind these efforts has been the high‐fidelity reproduction of material appearance attributes without one having to rely on the manipulation of ad hoc parameters. These models, however, are usually considered excessively time consuming for rendering and visualization applications requiring interactive rates. In this paper, we propose a strategy to address this open problem with respect to one of the most challenging of these organic materials, namely the human iris. More specifically, starting with the configuration of a predictive iridal light transport model on a parallel‐computing platform, we analyze the sensitivity of iridal appearance attributes to key model running parameters in order to achieve an optimal balance between fidelity and performance. We believe that the proposed strategy represents a step toward the real‐time and predictive synthesis of high‐fidelity iridal images for rendering and visualization applications, and it can be extended to other organic materials.

Appearance changes caused by light exposure provide important cues that impart a sense of realism to a computer-generated scene. For instance, a carpet may fade or wood may turn yellow over time as a result of many years of light exposure. In this paper, we analyse the key performance and accuracy trade-offs associated with the physically-based simulation of these phenomena. This analysis may be used to guide the selection of simulation parameters in order to achieve optimal color-accuracy and minimize runtime. We also propose a practical method to enable the predictive visualization of these phenomena within applications requiring interactive rates with minimal loss of accuracy. The effectiveness of the proposed techniques is demonstrated through simulations and image sequences depicting fading and yellowing caused by several years of exposure to light.

Exploration of the hyperspectral domain offers a host of new research and application possibilities involving material appearance modeling. In this article, we address these prospects with respect to human skin, one of the most ubiquitous materials portrayed in synthetic imaging. We present the first hyperspectral model designed for the predictive rendering of skin appearance attributes in the ultraviolet, visible, and infrared domains. The proposed model incorporates the intrinsic bio-optical properties of human skin affecting light transport in these spectral regions, including the particle nature and distribution patterns of the main light attenuation agents found within the cutaneous tissues. Accordingly, it accounts for phenomena that significantly affect skin spectral signatures, both within and outside the visible domain, such as detour and sieve effects, that are overlooked by existing skin appearance models. Using a first-principles approach, the proposed model computes the surface and subsurface scattering components of skin reflectance taking into account not only the wavelength and the illumination geometry, but also the positional dependence of the reflected light. Hence, the spectral and spatial distributions of light interacting with human skin can be comprehensively represented in terms of hyperspectral reflectance and BSSRDF, respectively.

The mineralogy and environmental history of Mars are been extensively investigated through remote sensing observations paired with laboratory and in situ experiments. A significant portion of these experiments is being devoted to the identification and quantification of different iron oxides present in the Martian terrains. Although such experiments can provide valuable information regarding the presence of these minerals, the scope of the resulting observations may be hindered by logistics and cost-related constraints. We believe that predictive computer simulations can be employed to mitigate some of these constraints and contribute to the generation and validation of hypotheses in this area. Accordingly, we propose the use of SPLITS (Spectral Light Transport Model for Sand) in investigations involving the spectral signatures of iron-rich sand-textured soils found on Mars, and demonstrate its predictive capabilities in this context through comparisons of modeled results with actual measured data.

In this paper, we investigate the effects of sand grain shape on the reflectance of sandy landscapes within the visible domain. Our investigation is supported by computer simulations performed using SPLITS (Spectral Light TransportModel for Sand) and taking into account actual sand characterization data. Our findings indicate that the spectral effects of grain shape may vary considerably depending on the distribution patterns of iron oxides present in sand-textured soils. These patterns and grain shape properties, namely roundness and sphericity, are largely determined by the formation processes of these soils. Hence, we believe that their interplay should be carefully taken into account in the retrieval of information about the mineralogy and morphology of sandy terrains.

The fading of materials due to light exposure over time is a major contributor to the overall aged appearance of man-made objects. Although much attention has been devoted to the modeling of aging and weathering phenomena over the last decade, comparatively little attention has been paid to fading effects. In this article, we present a theoretical framework for the physically based simulation of time-dependent spectral changes induced by absorbed radiation. This framework relies on the general volumetric radiative transfer theory, and it employs a physicochemical approach to account for variations in the absorptive properties of colorants. Employing this framework, a layered fading model that can be readily integrated into existing rendering systems is developed using the Kubelka-Munk theory. We evaluate its correctness through comparisons of measured and simulated fading results. Finally, we demonstrate the effectiveness of this model through renderings depicting typical fading scenarios.

Abstract The development of predictive appearance models for organic tissues is a challenging task due to the inherent complexity of these materials. In this paper, we closely examine the biophysical processes responsible for the appearance attributes of whole blood, one the most fundamental of these materials. We describe a new appearance model that simulates the mechanisms of light propagation and absorption within the cellular and fluid portions of this specialized tissue. The proposed model employs a comprehensive, and yet flexible first principles approach based on the morphological, optical and biochemical properties of blood cells. This approach allows for environment driven changes in the cells' anatomy and orientation to be appropriately included into the light transport simulations. The correctness and predictive capabilities of the proposed model are quantitatively and qualitatively evaluated through comparisons of modeled results with actual measured data and experimental observations reported in the scientific literature. Its incorporation into rendering systems is illustrated through images of blood samples depicting appearance variations controlled by physiologically meaningful parameters. Besides the contributions to the modeling of material appearance, the research presented in this paper is also expected to have applications in a wide range of biomedical areas, from optical diagnostics to the visualization and noninvasive imaging of blood‐perfused tissues.

The author has recently proposed a model to simulate light interactions with monocotyledonous (unifacial) leaves in the infrared domain. In this paper, we evaluate the applicability of this model to simulations performed in the visible (photosynthetic) domain, and aimed at the investigation of biophysical responses triggered by nutrient and water stress. The model's fidelity and predictability in this spectral domain are assessed through quantitative and qualitative comparisons of modeled results with measured data obtained for maize specimens. Its predictive capabilities are further demonstrated through the simulation of reflectance profiles resulting from experiments involving maize leaves under different water reduction procedures.

The authors have recently proposed a model, based on Monte Carlo methods, to simulate light interaction with sand. In this paper, principal component analysis and regression techniques are applied to yield a compact analytical representation of the spectral reflectance signatures produced by the model. This analytical formulation compares well with the original model and is appropriate for applications demanding interactive rates. Examples are provided comparing the original model to the proposed formulation for three hypothetical sand samples. The effect of water content on reflectance is demonstrated for these samples. Additionally, examples are provided comparing the original and proposed models for three sand samples from the U.S. Army Topographic Engineering Center spectral database.

We introduce a physiologically-based model for pupil light reflex (PLR) and an image-based model for iridal pattern deformation. Our PLR model expresses the pupil diameter as a function of the lighting of the environment, and is described by a delay-differential equation, naturally adapting the pupil diameter even to abrupt changes in light conditions. Since the parameters of our PLR model were derived from measured data, it correctly simulates the actual behavior of the human pupil. Another contribution of our work is a model for realistic deformation of the iris pattern as a function of pupil dilation and constriction. Our models produce high-fidelity appearance effects and can be used to produce real-time predictive animations of the pupil and iris under variable lighting conditions. We assess the predictability and quality of our simulations through comparisons of modeled results against measured data derived from experiments also described in this work. Combined, our models can bring facial animation to new photorealistic standards.

The authors have recently proposed a model, based on Monte Carlo methods, to simulate light interaction with sand. In this paper, principal component analysis (PCA) and regression techniques are applied to yield a compact analytical representation of the spectral reflectance signatures produced by the model. This analytical formulation compares well with the original model and is appropriate for applications demanding interactive rates.

Recent research efforts in image synthesis have been directed toward the rendering of believable and predictable images of biological materials. This course addresses an important topic in this area, namely the predictive simulation of skin's appearance. The modeling approaches, algorithms and data examined during this course can be also applied to the rendering of other organic materials such as hair and ocular tissues.

ABSTRACT Plasmas are ubiquitous in the Universe. An understanding of plasma phenomena is therefore of importance in almost every area of astrophysics, from stellar atmospheres to star clusters. Plasmas also occur in daily life both in industrial processes and in consumer products. Recent groundbreaking data is making this the golden age of plasma science. Although direct observations and analysis of data provide important physical evidence for plasma phenomena, they do not necessarily explain the phenomena. Hence, recent discoveries in this area might not only arise out of observations, but also from visual simulations of the phenomena supported by advanced rendering technologies. This report describes the state of art of such simulations, and examines practical issues often overlooked in the literature. Educational and public outreach applications are also discussed. Although the emphasis is on the predictive rendering of plasma processes, the simulation guidelines and trade‐offs addressed in this report can be extended to other types of natural phenomena. The report closes with a discussion of further avenues of research involving the visual simulation of plasma phenomena.

The retrieval of plant biophysical and biochemical properties from high spectral resolution data represents an active area of research within the remote sensing field. Scientific studies in this area are usually supported by computational simulations of light attenuation processes within foliar tissues. In heterogeneous organic materials, like plant leaves, sieve and detour effects can affect these processes and ultimately change the light gradients within these tissues and their spectral signatures. Although these effects have been extensively examined for applications involving the interactions of visible radiation with plant leaves, little is known about their role in the infrared domain. In this paper, we describe the procedural basis for their incorporation in the modeling of infrared-radiation transport (in the range of 750-2500 nm) within plant leaves. We also assess their impact on the predictability of simulation solutions relating the directionality of the incident radiation and the internal arrangement of the tissues to changes on foliar spectral signatures in this domain. Our investigation is grounded by the observations involving the modeled results and quantitative and qualitative data reported in the literature.

Due to the importance of plants in the Earth's ecosystem, their photobiological responses have become the subject of extensive research in life sciences. Leaf optical models have been developed to assist in the analysis of remotely sensed data to derive information on leaf biochemistry and anatomy from foliar spectral curves (transmittance and reflectance). In this paper, we investigate the implications of using in vitro pigment absorption spectra to model foliar optical properties in the visible domain. Typically, pigment absorption spectra have been determined using light absorption spectroscopy (AS) or by applying a data fitting approach. Alternatively, we propose the use of photoacoustic pigment AS, which, despite being available in the literature, has not been used in the modeling of foliar optical properties before. We also perform computational experiments in which foliar modeled spectral curves generated using these different absorption data sets are compared with actual measured data. Our findings indicate that the proposed alternative not only allows key pigments to be individually incorporated into the models, which, in turn, increases the predictability of the simulations, but also enables the generation of modeled foliar spectral curves that are more accurate than those obtained using absorption data derived from standard AS procedures.

Abstract Recently, light interactions with organic matter have become the object of detailed investigations by image synthesis researchers. Besides allowing these materials to be rendered in a more intuitive manner, these efforts aim to extend the scope of computer graphics applications to areas such as applied optics and biomedical imaging. There are, however, organic materials that still lack predictive simulation solutions. Among these, the ocular tissues, especially those forming the human iris, pose the most challenging modeling problems which are often associated with data scarcity. In this paper, we describe the first biophysically‐based light transport model for the human iris ever presented in the scientific literature. The proposed model algorithmically simulates the light scattering and absorption processes occurring within the iridal tissues, and computes the spectral radiometric responses of these tissues. Its design is based on the current scientific understanding of the iridal morphological and optical characteristics, and it is controlled by parameters directly related to these biophysical attributes. The accuracy and predictability of the spectral results provided by the model are evaluated through comparisons with actual measured iridal data, and its integration into rendering frameworks is illustrated through the generation of images depicting iridal chromatic variations. Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications

Abstract We describe a representation for tree leaves and an interactive modeling system for creating realistic close‐up images of leaf clusters. The planar outline of the leaf and the larger members of its venation system are strong factors in the recognition of plant species and as such are essential to realistic imaging. The larger veins also play a major biological role in determining the leaf surface shape and it is this role that we mimic in the shape modeling discussed in this paper. The proposed representation uses a model of a leaf consisting of a three‐dimensional skeleton formed by its larger veins and a surface membrane representing the leaf lamina that spans the void between the veins. The veins play two roles. They can be interactively modified to create the 3‐D shape of the leaf model. They also provide for realistic light and shadow effects when rendered as generalized cylinders using measured width parameters. The representation consists of two coupled data structures, a tree data structure of veins for the leaf skeleton and an unstructured triangular mesh for the leaf membrane. The skeleton is modified by the user of the modeling system, and the membrane mesh is a surface mesh that follows the skeleton shape computed using harmonic interpolation. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract This paper describes the modeling and simulation of the deformation of human skeletal muscle at different structural levels based on sound scientific principles, experimental evidence, and state‐of‐art muscle anatomy and physiology. The equations of a continuum model of a muscle with realistic architecture, including internal arrangement of muscle fibers and passive structures, and deformation, including activation relations, was developed and solved with the finite element method. The continuum model is used as the basis of a strategy for controlling muscle deformation using activation relations. In order to demonstrate the functionality of the model, it was used to investigate force production and structural changes during contraction of the human tibialis anterior for maximally and submaximally activated muscle behavior. From a comparison with experimental data obtained from ultrasound imaging, we concluded that the modeling and simulation of the continuum based on physiologically meaningful parameters as described in the paper is both an excellent predictor of force production observations and of changes in internal geometry under various test conditions. It is therefore a valuable tool for controlling muscle deformation during movement. Copyright © 2005 John Wiley & Sons, Ltd.

Simulating natural phenomena has always been a focal point for computer graphics research. Its importance goes beyond the production of appealing presentations, since research in this area can contribute to the scientific understanding of complex natural processes. The natural phenomena, known as the Aurora Borealis and Aurora Australis, are geomagnetic phenomena of impressive visual characteristics and remarkable scientific interest. Aurorae present a complex behavior that arises from interactions between plasma (hot, ionized gases composed of ions, electrons, and neutral atoms) and Earth's electromagnetic fields. Previous work on the visual simulation of auroral phenomena have focused on static physical models of their shape, modeled from primitives, like sine waves. In this article, we focus on the dynamic behavior of the aurora, and we present a physically-based model to perform 3D visual simulations. The model takes into account the physical parameters and processes directly associated with plasma flow, and can be extended to simulate the dynamics of other plasma phenomena as well as astrophysical phenomena. The partial differential equations associated with these processes are solved using a complete multigrid implementation of the electromagnetic interactions, leading to a simulation of the shape and motion of the auroral displays. In order to illustrate the applicability of our model, we provide simulation sequences rendered using a distributed forward mapping approach.

When light interacts with human skin, a complex and involved process begins as the light is absorbed and propagated by cells, fibers and other microscopic materials. This interaction happens countless times each day and its accurate simulation is essential to biomedical and computer graphics applications. Simulating this interaction is computationally intensive, yet highly suitable to parallelization. This paper describes the use of both a shared-memory high performance computer and a heterogeneous cluster to accelerate these simulations. With a description of the parallel software used, we present results to show the performance gains from using such a hybrid approach.

Abstract Despite the notable progress in physically‐based rendering, there is still a long way to go before we can automatically generate predictable images of biological materials. In this paper, we address an open problem in this area, namely the spectral simulation of light interaction with human skin. We propose a novel biophysically based model that accounts for all components of light propagation in skin tissues, namely surface reflectance, subsurface reflectance and transmittance, and the biological mechanisms of light absorption by pigments in these tissues. The model is controlled by biologically meaningful parameters, and its formulation, based on standard Monte Carlo techniques, enables its straightforward incorporation into realistic image synthesis frameworks. Besides its biophysically‐based nature, the key difference between the proposed model and the existing skin models is its comprehensiveness, i.e., it computes both spectral (reflectance and transmittance) and scattering (bidirectional surface‐scattering distribution function) quantities for skin specimens. In order to assess the predictability of our simulations, we evaluate their accuracy by comparing results from the model with actual skin measured data. We also present computer generated images to illustrate the flexibility of the proposed model with respect to variations in the biological input data, and its applicability not only in the predictive image synthesis of different skin tones, but also in the spectral simulation of medical conditions. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism

Abstract A mathematical model is presented to simulate the growth of a plant leaf. The tissue in the leaf has been regarded as a viscous, incompressible fluid whose 2D expansion comes from the non‐zero specific growth rate in area. The resulting system of equations are composed of the modified Navier‐Stokes equations. The level set method is used to capture the expanding leaf front. Numerical simulations indicate that different portions of the leaf expand at different rates, which is consistent with the biological observations in the growth of a plant leaf. Numerical results for the case of the Xanthium leaf growth are also presented. A standard ray tracing technique is applied to produce an animation simulating the leaf growth process of three days. The key results with their physical and practical implications are discussed. Copyright © 2004 John Wiley & Sons, Ltd.

Ground-based measurements of plant reflectance and transmittance are essential for remote sensing projects oriented toward agriculture, forestry, and ecology. This paper examines the application of principal components analysis (PCA) in the storage and reconstruction of such plant spectral data. A novel piecewise PCA approach (PPCA), which takes into account the biological factors that affect the interaction of solar radiation with plants, is also proposed. These techniques are compared through experiments involving the reconstruction of reflectance and transmittance curves for herbaceous and woody specimens. The spectral data used in these experiments were obtained from the Leaf Optical Properties Experiment (LOPEX) database. The reconstructions were performed aiming at a root-mean-square error lower than 1%. The results of these experiments indicate that PCA can effectively reduce the dimensionality of plant spectral databases from the visible to the infrared regions of the light spectrum, and that the PPCA approach can further maximize the accuracy/cost ratio of the storage and reconstruction of plant spectral reflectance and transmittance data.

Abstract We present the first computer graphics algorithm designed to simulate the aurora, a natural phenomenon of great visual beauty and considerable scientific interest. The algorithm is based on the current understanding of the physical origin of this natural display. The aurorae are mainly caused by high‐energy electrons originating in the sun and entering the earth's atmosphere in narrow regions centered on the magnetic poles. These electrons collide with atmospheric atoms, which are excited to higher energy levels. The excited atoms emit rapidly varying visible light in a curtain‐like volume as they return to lower energy levels, thereby creating the aurora. By simulating these light emissions along with the spatial and temporal distribution of the entering electrons, we are able to render the major visual aspects of auroral displays. The applicability of this auroral model for rendering and scientific purposes is illustrated through comparisons of synthetic images with photographs of real auroral displays. Copyright © 2003 John Wiley & Sons, Ltd.

The main topic of the paper is virtual spectrophotometers, virtual spectrophotometry and strategies for reducing the number of samples required to obtain asymptotically convergent measurements. A brief section on applications of virtual spectrophotometry is presented. This is followed by a careful formulation of virtual spectrophotometers. A virtual spectrophotometer, being a virtual device, is only described by its geometry.

We present an algorithm to simulate the aurora borealis, commonly known as the "northern lights", a natural phenomenon of great visual beauty and considerable scientific interest. The algorithm is based on the current understanding of the physical origin of the aurora. This natural display is mainly caused by high-energy electrons originating in the Sun and entering the Earth's atmosphere in narrow regions centered on the magnetic poles. These electrons collide with atmospheric atoms which are excited to higher energy levels. The excited atoms emit rapidly varying visible light in a curtain-like volume as they return to lower energy levels thereby creating the aurora. By simulating these light emissions along with the spatial and temporal distribution of the entering electrons, we are able to render the major visual aspects of auroral displays. This approach also allows the representation of time-dependent features that characterize the dynamic nature of the aurorae. The applicability of this auroral model for artistic and research purposes is illustrated through comparisons of synthetic images with photographs of real auroral displays.

Physically and biologically based reflectance and transmittance models add realism to image synthesis applications at the expense of a significant increase in rendering time. Current research efforts in this area focus on developing practical solutions to quickly access a BDF (which represents a combination of BRDF and BTDF) while preserving its original characteristics. In this paper an approach to reconstruct relatively complex isotropic BDFs is presented. The spectral curves obtained using the proposed approach are compared with measured spectral curves, and some issues regarding its performance and storage requirements are examined. Copyright © 1999 John Wiley & Sons, Ltd.

Physically and biologically based reflectance and transmittance models add realism to image synthesis applications at the expense of a significant increase in rendering time. Current research efforts in this area focus on developing practical solutions to quickly access a BDF (which represents a combination of BRDF and BTDF) while preserving its original characteristics. An approach to reconstruct arbitrary isotropic BDFs is presented. The spectral curves obtained using the proposed approach are compared with measured spectral curves, and some issues regarding its performance and storage requirements are examined.

Recent developments in rendering have provided very realistic images. However, these images rarely show organic objects. We believe that one of the main difficulties of rendering these objects realistically is the lack of reflectance and transmittance models oriented to organic materials. In this paper an algorithmic reflectance and transmittance model for plant tissue oriented to computer graphics applications is presented. The model accounts for the three components of light propagation in plant tissues, namely surface reflectance, subsurface reflectance and transmittance, and mechanisms of light absorption by pigments present in these tissues. The model design is based on the available biological information, and it is controlled by a small number of biologically meaningful parameters. Its formulation, based on standard Monte Carlo techniques, guarantees its easy incorporation into most rendering systems. The spectral curves of reflectance and transmittance computed by the model are compared with measured curves from actual experiments.

Abstract The parametric differential method calculates the form factors without using approximations by polygons. Because of this it contributes to the improvement of the realism of the images synthesised through the radiosity method. This paper presents the most important characteristics of the parametric differential method as well as the results concerning its accuracy. Comparison of the form factors found using the new algorithm has been made with those analytically found.