Artistic vision: painterly rendering using computer vision techniques

Journal ArticleWe present a method that takes a raster image as input and produces a painting-like image composed of strokes rather than pixels. Unlike previous automatic painting methods, we attempt to keep the number of brush-stroke small. This is accomplished by first segmenting the image into features, finding the medial axes points of these features, converting the medial axes points into ordered lists of image tokens, and finally rendering these lists as brush strokes. Our process creates images reminiscent of modern realist painters who often want an abstract or sketchy quality in their work

Automatic painting with economized strokes

Journal ArticleWe present a method that takes a raster image as input and produces a painting-like image composed of strokes rather than pixels. Unlike previous automatic painting methods, we attempt to use very few brush-strokes. This is accomplished by first segmenting the image into features, finding the medial axes points of these features, converting the medial axes points into ordered lists of image tokens, and finally rendering these lists as brush strokes. Our process creates images reminiscent of modern realist painters who often want an abstract or sketchy quality in their work

Practical surface light fields

The rendering of photorealistic surface appearance is one of the main challenges facing modern computer graphics. Image-based approaches have become increasingly important because they can capture the appearance of a wide variety of physical surfaces with complex reflectance behavior. In this dissertation, I focus on surface light fields, an image-based representation of view-dependent and spatially-varying appearance. Constructing a surface light field can be a time-consuming and tedious process. The data sizes are quite large, often requiring multiple gigabytes to represent complex reflectance properties. The result can only be viewed after a lengthy post-process is complete, so it can be difficult to determine when the light field is sufficiently sampled. Often, uncertainty about the sampling density leads users to capture many more images than necessary in order to guarantee adequate coverage. To address these problems, I present several approaches to simplify the capture of surface light fields. The first is a “human-in-the-loop” interactive feedback system based on the online svd. As each image is captured, it is incorporated into the representation in a streaming fashion and displayed to the user. In this way, the user receives direct feedback about the capture process, and can use this feedback to improve the sampling. To avoid the problems of discretization and resampling, I used incremental weighted least squares, a subset of radial basis function which allows for incremental local construction and fast rendering on graphics hardware. Lastly, I address the limitation of fixed lighting by describing a system that captures the surface light field of an object under synthetic lighting