Image Abstraction with Cartoonlike Shade Representation

by CGLab posted May 01, 2014
Extra Form
Author Gyeongrok Lee, Hochang Lee, Taemin Lee, Kyunghyun Yoon
Location WSCG 2012
Abstract Luminance quantization, which maps the luminance values of an image to discrete levels, is widely used for image abstraction and the expression of a cartoonlike effect. Existing luminance quantization techniques use each pixel’s luminance value separately, leading to a noisy image. Additionally, they do not take the shape of the imaged object into consideration. Thus, they suffer limitations in terms of cartoonlike shade representation. We propose a new luminance quantization algorithm that takes into account the shape of the image. We extract the silhouette from the image, compute edge-distance values, and incorporate this information intothe process of luminance quantization. We integrate the luminance values of neighboring pixels using an anisotropic filter, using gradient information for this filtering. As a result, ourquantized image is superior to that given by existing techniques.
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Image Abstraction with Cartoonlike Shade Representation

Gyeongrok Lee       Hochang Lee       Taemin Lee       Kyunghyun Yoon

CAU CGLab

IA_BIG1.JPG

Abstract

Luminance quantization, which maps the luminance values of an image to discrete levels, is widely used for image abstraction and the expression of a cartoonlike effect. Existing luminance quantization techniques use each pixel’s luminance value separately, leading to a noisy image. Additionally, they do not take the shape of the imaged object into consideration. Thus, they suffer limitations in terms of cartoonlike shade representation. We propose a new luminance quantization algorithm that takes into account the shape of the image. We extract the silhouette from the image, compute edge-distance values, and incorporate this information intothe process of luminance quantization. We integrate the luminance values of neighboring pixels using an anisotropic filter, using gradient information for this filtering. As a result, ourquantized image is superior to that given by existing techniques.