Comprehensible rendering of 3-D shapes
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Computer-generated pen-and-ink illustration
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Orientable textures for image-based pen-and-ink illustration
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Painterly rendering with curved brush strokes of multiple sizes
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
An algorithm for automatic painterly rendering based on local source image approximation
NPAR '00 Proceedings of the 1st international symposium on Non-photorealistic animation and rendering
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast primitive distribution for illustration
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A feature-based pencil drawing method
Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Stylized rendering of 3D scanned real world environments
Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering
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In this paper we present an algorithm to automatically produce artistic drawings from stereo image pairs. The input to the algorithm is a natural scene, along with a user-defined set of parameters that define the tone and stylistic properties of the image to be produced. The stereo analysis yields a depth map that is used to preserve the perspective perception of the stylized image. In the next step, we perform a local image contrast enhancement, which is driven by the color segmentation result of the original image. We then draw each stroke in a direction determined by the stereo-derived disparity layers, with the stroke density and distribution derived from a Poisson disc distribution. To outline important features in the image, we utilize the contour edges provided by an Edge Combination image. The output of the algorithm is a drawn-like form of the original scene, with objects highlighted by their dominant edges.