The transfer function bake-off

Pfister, Hanspeter; Lorensen, Bill; Bajaj, Chandrajit; Kindlmann, Gordon; Schroeder, Will; Avila, Lisa; Raghu, K.M.; Machiraju, Raghu; Lee, Jinho

doi:10.1109/38.920623

Cited by 243 publications

(118 citation statements)

References 3 publications

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“…The algorithm requires the features to be defined before compression. These features can be identified manually or by automatic feature detection tools [18]. To save disk storage space and to overcome the limitation of I/O bandwidth in network systems, a series of compressed frames are read from data source servers to browsing clients.…”

Section: Introductionmentioning

confidence: 99%

Volumetric video compression for interactive playback

Sohn

Bajaj

Siddavanahalli

2004

Computer Vision and Image Understanding

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We develop a volumetric video system which supports interactive browsing of compressed timevarying volumetric features (significant isosurfaces and interval volumes). Since the size of even one volumetric frame in a time-varying 3D data set is very large, transmission and on-line reconstruction are the main bottlenecks for interactive remote visualization of time-varying volume and surface data. We describe a compression scheme for encoding time-varying volumetric features in a unified way, which allows for on-line reconstruction and rendering. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and encode only the significant blocks that contribute to the isosurfaces and interval volumes. The results show that our compression scheme achieves high compression ratio with fast reconstruction, which is effective for client-side rendering of time-varying volumetric features.

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Section: Introductionmentioning

confidence: 99%

Volumetric video compression for interactive playback

Sohn

Bajaj

Siddavanahalli

2004

Computer Vision and Image Understanding

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“…Automatic transfer function generation is not the focus of this paper and other transfer function generation methods may be considered. Refer to [19] for an overview of different methods. The technique of Kindlmann and Durkin is designed for a class of scalar volume data where the regions of interest are boundaries between different materials.…”

Section: Generation Of Input Imagesmentioning

confidence: 99%

Explorable images for visualizing volume data

Tikhonova

Correa

2010

2010 IEEE Pacific Visualization Symposium (PacificVis)

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We present a technique which automatically converts a small number of single-view volume rendered images of the same 3D data set into a compact representation of that data set. This representation is a multi-layered image, or an explorable image, which enables interactive exploration of volume data in transfer function space without accessing the original data. We achieve this by automatically extracting layers depicted in composited images. The layers can then be recombined in different ways to simulate opacity changes and recoloring of individual features. Our results demonstrate that explorable images are especially useful when the volume data is too large for interactive exploration, takes too long to render due to the underlying mesh structure or desired shading effect, or if the original volume data is not available. Explorable images can offer real-time image-based interaction as a preview mechanism for remote visualization or visualization of large volume data on low-end hardware, within a mobile device, or a Web browser.

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“…Section 3), including: route planning [70], (robot) navigation [28,56], video surveillance [69,71], handwriting recognition [72], internal radiation therapy [13], image segmentation [10,11] (see also Fig. (1)), skeletonization [73], Bouligand-Minkowsky fractal dimension [74], neuromorphometry [73,75], MRI data analysis [76], and volume rendering [77]. Some application areas of DT were simply explored in alternative ways, some were brand new (cf.…”

Section: The Last Decadementioning

confidence: 99%

“…In the last case, DT are either applied by themselves or as an intermediate method such as: route planning and (robot) navigation [26,28,56,61,70], collision prevention [27], video surveillance [69,71], internal radiation therapy [13], handwriting recognition [62,72], (medical) image segmentation [9][10][11][13][14][15] (see also Fig. (1)), skeletonization [63,73], Voronoi tessellations [30,64], BouligandMinkowsky fractal dimension [74], Watershed algorithms [65], neuromorphometry [73,75], MRI data analysis [76,92], and volume rendering [36,77], to mention but a few.…”

Section: Current and Future Developmentsmentioning

confidence: 99%

Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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Abstract:In image and video analysis, distance transformations (DT) are frequently used. They provide a distance image (DI) of background pixels to the nearest object pixel. DT touches upon the core of many applications; consequently, not only science but also industry has conducted a significant body of work in this field. However, in a vast majority of the cases this has not been published in major scientific outlets but has been filed as a patent application. This article provides a brief introduction into DT, including a specification of a few of the most prominent algorithms in the field. Next, a few interesting algorithms from the last decade are discussed. A benchmark including eight DT algorithms (i.e., city block, Danielsson's algorithm, chamfer 3-4, hexadecagonal region growing, a recent claimed true Euclidean DT, and three exact Euclidean DT) has been executed, which illustrates the intriguing complexity of DT in terms of precision and computational complexity. Subsequently, a selection of key patent applications are discussed that have emerged in this field, including their scientific merit and areas of application. Finally, this article's findings are summarized and discussed, with an emphasis on both the common ground of scientific articles and patent applications as well as the added value they can have to each other.

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The transfer function bake-off

Cited by 243 publications

References 3 publications

Volumetric video compression for interactive playback

Volumetric video compression for interactive playback

Explorable images for visualizing volume data

Distance Transforms: Academics Versus Industry

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