Display of Surfaces from Volume Data
IEEE Computer Graphics and Applications
Comprehensible rendering of 3-D shapes
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
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Proceedings of the 1996 symposium on Volume visualization
Semi-automatic generation of transfer functions for direct volume rendering
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
Volume illustration: non-photorealistic rendering of volume models
Proceedings of the conference on Visualization '00
Proceedings of the conference on Visualization '01
Probabilistic surfaces: point based primitives to show surface uncertainty
Proceedings of the conference on Visualization '02
IEEE Transactions on Visualization and Computer Graphics
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
Image enhancement by unsharp masking the depth buffer
ACM SIGGRAPH 2006 Papers
Illustrating surfaces in volume
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
The gradient of the maximal curvature estimation for crest lines extraction
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
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We present a novel Procedural Image Processing (PIP) method and demonstrate its applications in visualization. PIP modulates the sampling positions of a conventional image processing kernel (e.g. edge detection filter) through a procedural perturbation function. When properly designed, PIP can produce a variety of styles for edge depiction, varying on width, solidity, and pattern, etc. In addition to producing artistic stylization, in this paper we demonstrate that PIP can be employed to achieve various visualization tasks, such as contour enhancement, focus+context visualization, importance driven visualization and uncertainty visualization. PIP produces unique effects that often either cannot be easily achieved through conventional filters or would require multiple pass filtering. PIP perturbation functions are either defined by analytical expressions or encoded in pre-generated images. We leverage the programmable fragment shader of the current graphics hardware for achieving the operations in real-time.