Morphological Pyramids in Multiresolution MIP Rendering of Large Volume Data: Survey and New Results
Journal of Mathematical Imaging and Vision
Acceleration Techniques for GPU-based Volume Rendering
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Point-based rendering techniques
Computers and Graphics
GPU-ABiSort: optimal parallel sorting on stream architectures
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Frequency domain volume rendering by the wavelet X-ray transform
IEEE Transactions on Image Processing
Nonlinear multiresolution signal decomposition schemes. I. Morphological pyramids
IEEE Transactions on Image Processing
Multiresolution maximum intensity volume rendering by morphological adjunction pyramids
IEEE Transactions on Image Processing
Multiresolution maximum intensity volume rendering by morphological pyramids
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Mathematical morphology in computer graphics, scientific visualization and visual exploration
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Streaming-enabled parallel dataflow architecture for multicore systems
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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This paper is concerned with a multiresolution representation for maximum intensity projection (MIP) volume rendering based on morphological pyramids which allows progressive refinement. We consider two algorithms for progressive rendering from the morphological pyramid: one which projects detail coefficients level by level, and a second one, called streaming MIP, which resorts the detail coefficients of all levels simultaneously with respect to decreasing magnitude of a suitable error measure. The latter method outperforms the level-by-level method, both with respect to image quality with a fixed amount of detail data, and in terms of flexibility of controlling approximation error or computation time. We improve the streaming MIP algorithm, present a GPU implementation for both methods, and perform a comparison with existing CPU and GPU implementations.