No abstract

This paper presents models for graphite pencil, drawing paper, blenders, and kneaded eraser that produce realistic looking pencil marks, textures, and tones. Our models are based on an observation of how lead pencils interact with drawing paper, and on the absorptive and dispersive properties of blenders and erasers interacting with lead material deposited over drawing paper. The models consider parameters such as the particle composition of the lead, the texture of the paper, the position and shape of the pencil materials, and the pressure applied to them. We demonstrate the capabilities of our approach with a variety of images and compare them to digitized pencil drawings. We also present image‐based rendering results implementing traditional graphite pencil tone rendering methods.

Researchers in non‐photorealistic rendering have investigated the display of three‐dimensional worlds using various display models. In particular, recent work has focused on the modeling of traditional artistic media and styles such as pen‐and‐ink illustration and watercolor painting. By providing 3D rendering systems that use these alternative display models users can generate traditional illustration renderings of their three‐dimensional worlds. In this paper we present our graphite pencil 3D renderer. We have broken the problem of simulating pencil drawing down into four fundamental parts: (1) simulating the drawing materials (graphite pencil and drawing paper, blenders and kneaded eraser), (2) modeling the drawing primitives (individual pencil strokes and mark‐making to create tones and textures), (3) simulating the basic rendering techniques used by artists and illustrators familiar with pencil rendering, and (4) modeling the control of the drawing composition. Each part builds upon the others and is essential to developing the framework for higher‐level rendering methods and tools. In this paper we present parts 2, 3, and 4 of our research. We present non‐photorealistic graphite pencil rendering methods for outlining and shading. We also present the control of drawing steps from preparatory sketches to finished rendering results. We demonstrate the capabilities of our approach with a variety of images generated from 3D models.

No abstract

Figure 1: The progression of our algorithm while segmenting the brain matter in a 256 3 head MRI with a signal-to-noise ratio of 11. Our algorithm interactively computes this segmentation in 7 seconds -14× faster than previous GPU algorithms with no reduction in accuracy.Abstract We present a novel GPU level set segmentation algorithm that is both work-efficient and step-efficient. Our algorithm has O(log n) step-complexity, in contrast to previous GPU algorithms [Lefohn et al. 2004;Jeong et al. 2009] which have O(n) step-complexity. Moreover our algorithm limits the active computational domain to the minimal set of changing elements by examining both the temporal and spatial derivatives of the level set field. We apply our algorithm to 3D medical images ( Figure 1) and demonstrate that our algorithm reduces the total number of processed level set field elements by 16× and is 14× faster than previous GPU algorithms with no reduction in segmentation accuracy.Introduction Identifying distinct regions in images -a task known as segmentation -is an important task in computer vision and medical imaging. The GPU narrow band algorithm for level set segmentation can compute highly accurate segmentation results for noisy medical images and dramatically reduces computation times compared to optimized CPU implementations. However the GPU narrow band solver we tested took over 100 seconds converge on the brain matter in a 256 3 head MRI on an Nvidia GTX 280 ( Figure 2). This limitation constrains clinical applications and motivates our work-efficient algorithm.