Bruce Gooch

Color transfer between images

We use a simple statistical analysis to impose one images color characteristics on another. We can achieve color correction by choosing an appropriate source image and apply its characteristic to another image.

Real-time video abstraction

We present an automatic, real-time video and image abstraction framework that abstracts imagery by modifying the contrast of visually important features, namely luminance and color opponency. We reduce contrast in low-contrast regions using an approximation to anisotropic diffusion, and artificially increase contrast in higher contrast regions with difference-of-Gaussian edges. The abstraction step is extensible and allows for artistic or data-driven control. Abstracted images can optionally be stylized using soft color quantization to create cartoon-like effects with good temporal coherence. Our framework design is highly parallel, allowing for a GPU-based, real-time implementation. We evaluate the effectiveness of our abstraction framework with a user-study and find that participants are faster at naming abstracted faces of known persons compared to photographs. Participants are also better at remembering abstracted images of arbitrary scenes in a memory task.

A non-photorealistic lighting model for automatic technical illustration

Phong-shaded 3D imagery does not provide geometric information of the same richness as human-drawn technical illustrations. A non-photorealistic lighting model is presented that attempts to narrow this gap. The model is based on practice in traditional technical illustration, where the lighting model uses both luminance and changes in hue to indicate surface orientation, reserving extreme lights and darks for edge lines and highlights. The lighting model allows shading to occur only in mid-tones so that edge lines and highlights remain visually prominent. In addition, we show how this lighting model is modified when portraying models of metal objects. These illustration methods give a clearer picture of shape, structure, and material composition than traditional computer graphics methods. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.

Non-Photorealistic Rendering

The ubiquity of computer-generated imagery around us, in movies, advertising or on the Internet is already being taken for granted and what impresses most people is the photorealistic quality of the images. Pictures, as we have often been told, are worth a thousand words and the information transported by an image can take many different forms. Many computer graphics researchers are exploring non-photorealistic rendering techniques as an alternative to realistic rendering. Defined by what it is not, non-photorealistic rendering brings art and science together, concentrating less on the process and more on the communication content of an image. Techniques that have long been used by artists can be applied to computer graphics to emphasize subtle attributes and to omit extraneous information. This book provides an overview of the published research on non-photorealistic rendering in order to categorize and distill the current research into a body of usable techniques. A summary of some of the algorithms as well as pseudo-code for producing some of the images is included.

Interactive technical illustration

A rendering is an abstraction that favors, preserves, or even emphasizes some qualities while sacrificing, suppressing, or omitting other characteristics that are not the focus of attention. Most computer graphics rendering activities have been concerned with photorealism, i.e., trying to emulate an image that looks like a highquality photograph. This laudable goal is useful and appropriate in many applications, but not in technical illustration where elucidation of structure and technical information is the preeminent motivation. This calls for a different kind of abstraction in which technical communication is central, but art and appearance are still essential instruments toward this end. Work that has been done on computer generated technical illustrations has focused on static images, and has not included all of the techniques used to hand draw technical illustrations. A paradigm for the display of technical illustrations in a dynamic environment is presented. This display environment includes all of the benefits of computer generated technical illustrations, such as a clearer picture of shape, structure, and material composition than traditional computer graphics methods. It also includes the three-dimensional interactive strength of modern display systems. This is accomplished by using new algorithms for real time drawing of silhouette curves, algorithms which solve a number of the problems inherent in previous methods. We incorporate current non-photorealistic lighting methods, and augment them with new shadowing algorithms based on accepted techniques used by artists and studies carried out in human perception. This paper, all of the images, and a mpeg video clip are available at http://www.cs.utah.edu/ bgooch/ITI/. CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.

Color2Gray: salience-preserving color removal

Visually important image features often disappear when color images are converted to grayscale. The algorithm introduced here reduces such losses by attempting to preserve the salient features of the color image. The Color2Gray algorithm is a 3-step process: 1) convert RGB inputs to a perceptually uniform CIE L*a*b* color space, 2) use chrominance and luminance differences to create grayscale target differences between nearby image pixels, and 3) solve an optimization problem designed to selectively modulate the grayscale representation as a function of the chroma variation of the source image. The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.

Human facial illustrations: Creation and psychophysical evaluation

We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.

Artistic Vision: painterly rendering using computer vision techniques

We present a method that takes a raster image as input and produces a painting-like image composed of strokes rather than pixels. Our method works by first segmenting the image into features, finding the approximate medial axes of these features, and using the medial axes to guide brush stroke creation. System parameters may be interactively manipulated by a user to effect image segmentation, brush stroke characteristics, stroke size, and stroke frequency. This process creates images reminiscent of those contemporary representational painters whose work has an abstract or sketchy quality. Our software is available at http://www.cs.utah.edu/npr/ArtisticVision.

Artistic Composition for Image Creation

Altering the viewing parameters of a 3D object results in computer graphics images of varying quality. One aspect of image quality is the composition of the image. While the esthetic properties of an image are subjective, some heuristics used by artists to create images can be approximated quantitatively. We present an algorithm based on heuristic compositional rules for finding the format, viewpoint, and layout for an image of a 3D object. Our system computes viewing parameters automatically or allows a user to explicitly manipulate them.

Retargeting Images and Video for Preserving Information Saliency

A nonphotorealistic algorithm for retargeting images adapts large images so that important objects in the image are still recognizable when displayed at a lower target resolution. Unlike existing image manipulation techniques such as cropping and scaling, the retargeting algorithm can handle multiple important objects in an image. To identify the important objects in an image, we must first segment the image. We use mean-shift image segmentation to decompose an image into homogeneous regions.