We introduce a semi-automatic lighting design method that deploys per-voxel accessory lights (fill and detail lights) to enhance local shapes, as well as to increase the perceptibility and visual saliency of an object. Our approach allows the user to manually design arbitrary lights in a scene for creating the desired feeling of emotion. The user designed lights are used as key lights and our approach automatically configures per-voxel accessory lights that preserve the user designed feeling of emotion. Per-voxel fill lights brighten the shadows and thus increase the perceptibility and visual saliency. Per-voxel detail lights enhance the visual cues for the local shape perception. Moreover, the revealed local shapes are controlled by the user employing an importance distribution. Similarly, the perceptibility and visual saliency are also controlled based on an importance distribution. Our perceptual measurement guarantees that the revealed local shapes are independent of the key lights. In addition, our method provides two control parameters, which adjust the fill and detail lights, to provide the user with additional flexibility in designing the expected lighting effect. The major contributions of this paper are the idea of using the importance distribution to control local shapes, the per-voxel accessory lights and the perceptual measurement.

We introduce a lighting system that enhances the visual cues in a rendered image for the perception of 3D volumetric objects. We divide the lighting effects into global and local effects, and deploy three types of directional lights: the key light and accessory lights (fill and detail lights). The key light provides both lighting effects and carries the visual cues for the perception of local and global shapes and depth. The cues for local shapes are conveyed by gradient; those for global shapes are carried by shadows; and those for depth are provided by shadows and translucent objects. Fill lights produce global effects to increase the perceptibility. Detail lights generate local effects to improve the cues for local shapes. Our method quantifies the perception and uses an exhaustive search to set the lights. It configures accessory lights with the consideration of preserving the global impression conveyed by the key light. It ensures the feeling of smooth light movements in animations. With simplification, it achieves interactive frame rates and produces results that are visually indistinguishable from results using the nonsimplified algorithm. The major contributions of this paper are our lighting system, perception measurement and lighting design algorithm with our indistinguishable simplification.

Abstract This paper introduces an automatic color design method that is driven by an importance function of the objects within a volumetric dataset. Our method allows the user to intuitively modify the object classification and the importance distribution function in the 2D rendered image. It automatically computes the transfer function, especially the color distribution, to convey the importance of the objects. In our approach, the importance of an object is represented as the attentiveness of a color. In addition, we preserve the color harmony in the rendered image in order to provide a visually pleasing result. In this paper, we propose a set of computational measurements to compute the color attentiveness and color harmony. Our color assignment algorithm supports arbitrary‐dimensional transfer functions and obtains interactive frame rates. Our method involves three color spaces, namely Coloroid system, CIE LChuv, and Adobe RGB color space. It calculates the color attentiveness in CIE LChuvspace, and the color harmony in Coloroid system. It, then, assigns the transfer function in a dual space of Adobe RGB space and renders the resulting image in Adobe RGB space. We conducted a detailed user study, which proves that our method successfully conveys the importance distributions. Our contribution in this paper is not only our importance driven approach, but also our computational measurements and our color assignment algorithm.

We introduce a simple, yet powerful method called the Cumulative Heat Diffusion for shape-based volume analysis, while drastically reducing the computational cost compared to conventional heat diffusion. Unlike the conventional heat diffusion process, where the diffusion is carried out by considering each node separately as the source, we simultaneously consider all the voxels as sources and carry out the diffusion, hence the term cumulative heat diffusion. In addition, we introduce a new operator that is used in the evaluation of cumulative heat diffusion called the Volume Gradient Operator (VGO). VGO is a combination of the LBO and a data-driven operator which is a function of the half gradient. The half gradient is the absolute value of the difference between the voxel intensities. The VGO by its definition captures the local shape information and is used to assign the initial heat values. Furthermore, VGO is also used as the weighting parameter for the heat diffusion process. We demonstrate that our approach can robustly extract shape-based features and thus forms the basis for an improved classification and exploration of features based on shape.

We introduce a modified dendrogram (MD) (with subtrees to represent clusters) and display it in 2D for multidimensional transfer function design. Such a transfer function for direct volume rendering employs a multidimensional space, termed attribute space. The MD reveals the hierarchical structure information of the attribute space. The user can design a transfer function in an intuitive and informative manner using the MD user interface in 2D instead of multidimensional space, where it is hard to ascertain the relationship of the space. In addition, we provide the capability to interactively modify the granularity of the MD. The coarse-grained MD primarily shows the global information of the attribute space while the fine-grained MD reveals the finer details, and the separation ability of the attribute space is completely preserved in the finest granularity. With this so called multigrained method, the user can efficiently create a transfer function using the coarse-grained MD, and then fine tune it with the fine-grained MDs. Our method is independent on the type of the attributes and supports arbitrary-dimension attribute space.