Gladimir V. G. Baranoski

A Biophysically-Based Spectral Model of Light Interaction with Human Skin

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.

Photorealistic models for pupil light reflex and iridal pattern deformation

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.

An Algorithmic Reflectance and Transmittance Model for Plant Tissue

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.

Virtual spectrophotometric measurements for biologically and physically-based rendering

The group of measurements necessary to characterize both the color and surface finish of an object is called the measurement of appearance of an object [4]. This group of measurements involves the spectral energy distribution of propagated light, measured in terms of reflectance and transmittance, and the spatial distribution of that light, measured in terms of the bidirectional reflectance distribution function (BRDF) and the bidirectional transmittance distribution function (BTDF). The variations in the spectral energy distribution affect appearance characteristics such as hue, lightness and saturation, while the changes in the spatial distribution affect appearance characteristics such as gloss, reflection haze, transmission haze, luster and translucency as noted by Hunter and Harold [4]. Measuring these appearance characteristics is crucial for realistic rendering.

Interactive venation-based leaf shape modeling

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

A Predictive Light Transport Model for the Human Iris

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

Efficiently simulating scattering of light by leaves

Rendering techniques currently used in computer graphics enable the generation of very realistic images for a wide range of materials. Despite the latest achievements in this field, however, there are still areas, such as biological imaging, open for further investigation. In this paper an efficient approach for simulating light scattering by leaves is presented. It consists of pre-computing reflectance and transmittance values and applying a simplified scattering model for foliar tissues. This model accounts for the main biological characteristics of these organic materials, while avoiding undue complexity in order to increase the efficiency of the light scattering simulations. Its design and formulation are based on standard Monte Carlo methods, and it can be incorporated into global illumination systems without a significant computational overhead. The accuracy and performance of the proposed scattering model are examined through comparisons with a more detailed biologically based model whose spectral readings have been verified against experimental data for real specimens.

A two-stage approach for discriminating melanocytic skin lesions using standard cameras

In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and pheomelanin, can potentially be used in the computation of relevant features to differentiate these lesions. So, we describe how these features can be estimated using only standard camera images. Moreover, we demonstrate that using these features in conjunction with features based on the well known ABCD rule, it is possible to achieve 100% of sensitivity and more than 99% accuracy in melanocytic skin lesion discrimination, which is a highly desirable characteristic in a prescreening system.

A novel approach for simulating light interaction with particulate materials: application to the modeling of sand spectral properties

In this paper, we present a new spectral light transport model for sand. The model employs a novel approach to simulate light interaction with particulate materials which yields both the spectral and spatial (bi-directional reflectance distribution function, or BRDF) responses of sand. Furthermore, the parameters specifying the model are based on the physical and mineralogical properties of sand. The model is evaluated quantitatively, through comparisons with measured data. Good spectral reconstructions were achieved for the reflectances of several real sand samples. The model was also evaluated qualitatively, and compares well with descriptions found in the literature. Its potential applications include, but are not limited to, applied optics, remote sensing and image synthesis.

Reducing the dimensionality of plant spectral databases

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.