Non-photorealistic rendering from multiple images. Bartesaghi, A., Sapiro, G., Malzbender, T., & Gelb, D. In Proceedings - International Conference on Image Processing, ICIP, volume 4, pages 2403-2406, 2004. ![Non-photorealistic rendering from multiple images [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex A new paradigm for automatic nonphotorealistic rendering (NPR) is introduced in this paper. Existing NPR approaches can be categorized in two groups depending on the type of input they use: image based and object based. Using multiple images as input to the NPR scheme, we propose a novel hybrid model that simultaneously uses information from the image and object domains. The benefit not only comes from combining the features of each approach, but most important, it minimizes the need for manual or user assisted tasks in extracting scene features and geometry, as employed in virtually all state-of-the-art NPR approaches. We describe a particular implementation of such an hybrid system and present a number of automatically generated pen-and-ink style drawings. This work then shows how to use and extend well developed techniques in computer vision to address fundamental problems in image representation and rendering.
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abstract = {A new paradigm for automatic nonphotorealistic rendering (NPR) is introduced in this paper. Existing NPR approaches can be categorized in two groups depending on the type of input they use: image based and object based. Using multiple images as input to the NPR scheme, we propose a novel hybrid model that simultaneously uses information from the image and object domains. The benefit not only comes from combining the features of each approach, but most important, it minimizes the need for manual or user assisted tasks in extracting scene features and geometry, as employed in virtually all state-of-the-art NPR approaches. We describe a particular implementation of such an hybrid system and present a number of automatically generated pen-and-ink style drawings. This work then shows how to use and extend well developed techniques in computer vision to address fundamental problems in image representation and rendering.},
bibtype = {inProceedings},
author = {Bartesaghi, Alberto and Sapiro, Guillermo and Malzbender, Tom and Gelb, Dan},
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