What are some of the techniques used to reveal insights into volumetric data?

exploded views or high-level abstraction strategies, amongst others, are used to reveal insights and represent essential structures of the volume in a clear way while less important details are subjugated.

How can the authors get the structured direction of the model?

Once the most structured direction of the model has been selected, a sequence of perpendicular partitions in that direction can be obtained using a simple greedy algorithm.

What is the only interaction of the user with the data?

The only necessary interaction of the user with the data is a single threshold parameter which determines when the partitioning (or grouping) has to stop.

What is the main limitation of volume sculpting?

Volume illustration techniques enhance the perception of structure, shape, orientation, and depth relationships in a volume model.

What is the entropy rate of a sequence?

The entropy rate is also a measure of the compressibility of a sequence: the higher the uncertainty, the lower the compressibility.

What is the way to re-organize the parts?

Their method uses a continuous degree-of-interest function to distinguish between focus and context and is capable of re-arranging the parts dynamically based on the viewpoint.

What is the viewpoint for evaluating a volume?

Vàzquez et al. [8] have introduced the viewpoint entropy as a measure for viewpoint quality evaluation, where the best viewpoint is defined as the one that has maximum entropy.

How do the authors obtain the partitions of the model?

Such partitions will be obtained using two complementary approaches: a top-down strategy that divides the model according to the maximum information gain and a bottom-up method that joins the slices according to a similarity criterion.

How many slabs have been used in the tests?

In all the tests a sphere of 42 viewpoints has been used and the stopping criterion has been fixed by the number of slabs entered by the user.

What is the joint probability distribution of X and Y?

Given two slices A and B from the volume dataset, with associated random variables X and Y , the joint probability distribution p(x, y) can be estimated by simple normalization of the joint histogram h(x, y) of both slices.

What is the gain of information when a dataset is divided into two parts?

The gain of information when a model is divided into two parts is given by the dissimilarity between them (measured by JS) weighted by their relative volume.

What is the entropy rate of a block of L consecutive symbols?

The joint entropy of a block of L consecutive symbols or L-block entropy is defined byH(XL) = − ∑xL∈XL p(xL) log p(xL), (2)where the sum runs over all possible L-blocks.

Open AccessBook Chapter10.1007/978-3-540-85412-8_14

Similarity-Based Exploded Views

M. Ruiz, +5 more

- 27 Aug 2008

- pp 154-165

TL;DR: A volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other and the thickness of slabs is driven by the similarity between partitions.

Abstract: Exploded views are often used in illustration to overcome the problem of occlusion when depicting complex structures. In this paper, we propose a volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other. The thickness of slabs is driven by the similarity between partitions. We use an information-theoretic technique for the generation of exploded views. First, the algorithm identifies the viewpoint which gives the most structured view of the data. Then, the partition of the volume into the most informative slabs for exploding is obtained using two complementary similarity-based strategies. The number of slabs and the similarity parameter are freely adjustable by the user.

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Similarity-based exploded views" ?

In this paper, the authors propose a volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other.

2. What have the authors stated for future works in "Similarity-based exploded views" ?

In their future work the authors will study how many slices should be presented and whether additional information about the similarity distances can help the user for an optimal understanding of the volume.

3. What is the common strategy for communicating the structure of complex 3D objects?

Exploded views and deformations are a common strategy for communicating the structure of complex 3D objects that are composed of many subparts.

4. What is the way to preserve the context of volume?

Their context-preserving volume rendering model uses a function ofshading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions.

Fig. 5. (a) The volume partitioning and (b) the slice grouping approaches applied to a CT-scan of a patient with a brain hemorrhage. An histological data model decomposed with the (c) volume partitioning and (d) slice grouping methods.

Fig. 3. Different volumes (first row) and their corresponding most structured views (second row). From left to right: (a) a synthetic model, (b) a CT-scan of a patient with hemorrhage, (c) a CT-scan of a tooth and (d) a CT-scan of the human body.

Table 1. Time cost (seconds) for computing the most structured view, and for volume partitioning (P) and slice grouping (G) in 2, 4, and 8 slabs, respectively.

Fig. 1. Main steps of the selection of the most structured view. (a) Sphere of viewpoints, (b) sampling process for one viewpoint, (c) samples considered for the entropy rate computation, and (d) colored viewpoint sphere (using a thermic scale, from blue to red) representing the values of the viewpoint entropy rate.

Fig. 4. (a) The volume partitioning and (b) the slice grouping approaches applied to a CT scan of the human body.

Fig. 2. (i) Top-down volume partition: (i.a) partitioning planes are taken perpendicular to the most structured view, (i.b) dissimilarity between subvolumes is given by the Jensen-Shannon divergence, and (i.c) examples showing two different partitions. (ii) Bottom-up volume partition: (ii.a) slices are taken perpendicular to the most structured view direction, (ii.b) similarity between slices or slabs is computed using mutual information, and (ii.c) two examples resulting from the grouping process.

Citations

Proceedings Article•10.1145/1809939.1809942

Compact explosion diagrams

Markus Tatzgern, +2 more

- 07 Jun 2010

TL;DR: This paper presents a system to automatically generate compact explosion diagrams, Inspired by handmade illustrations, which reduces the complexity of an explosion diagram by rendering an exploded view only for a subset of the assemblies of an object.

...read moreread less

Journal Article•10.1109/TVCG.2010.151

Exploded View Diagrams of Mathematical Surfaces

O Karpenko, +3 more

- 01 Nov 2010

- IEEE Transactions on Visualization and C...

TL;DR: This work presents a technique for visualizing complicated mathematical surfaces that is inspired by hand-designed topological illustrations and generates exploded views that expose the internal structure of such a surface by partitioning it into parallel slices, which are separated from each other along a single linear explosion axis.

...read moreread less

•Proceedings Article•10.1145/3489849.3489851

ImNDT: Immersive Workspace for the Analysis of Multidimensional Material Data From Non-Destructive Testing

Alexander Gall, +2 more

- 08 Dec 2021

TL;DR: In this paper, an immersive visualization system for fiber-reinforced polymers (FRPs) is presented, which offers material experts an immersive exploration of multidimensional secondary data of FRPs.

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Journal Article•10.1007/S00170-017-0414-Y

Hierarchical exploded view generation based on recursive assembly sequence planning

Jiapeng Yu, +1 more

- 09 Jun 2017

- The International Journal of Advanced Ma...

TL;DR: In this paper, a method for automatic generation of hierarchical exploded view for complex products based on recursive search of multi-level assembly sequences is proposed, where the whole assembly sequence planning task is subdivided into the subtasks of the customized levels so that the complexity of a large-scale sequence planning is reduced.

...read moreread less

•Book Chapter•10.1007/978-3-642-33179-4_33

Context-Preserving Volumetric Data Set Exploration Using a 3D Painting Metaphor

Lev Faynshteyn, +1 more

- 16 Jul 2012

TL;DR: A technique for fast and intuitive visualization and exploration of unsegmented volumetric data sets that uses a single, consistent interaction style to achieve desired contextual views and perform several types of volume exploration and editing operations is presented.

...read moreread less

...

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References

•Book

Elements of information theory

Thomas M. Cover, +1 more

- 01 Jan 1991

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.

...read moreread less

52.2K

•Journal Article•10.1109/42.563664

Multimodality image registration by maximization of mutual information

Frederik Maes, +4 more

- 01 Apr 1997

- IEEE Transactions on Medical Imaging

TL;DR: The results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

...read moreread less

Journal Article•10.1023/A:1007958904918

Alignment by Maximization of Mutual Information

Paul A. Viola, +1 more

- 01 Sep 1997

- International Journal of Computer Vision

TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.

...read moreread less

The information bottleneck method

Naftali Tishby, +2 more

- 24 Apr 2000

TL;DR: The variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.

...read moreread less

•Book

Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

Thomas M. Cover, +1 more

- 01 Jul 2006

2.2K

...

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Similarity-Based Exploded Views

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AI Agents for this Paper

Most frequently asked questions

1. What are the contributions mentioned in the paper "Similarity-based exploded views" ?

2. What have the authors stated for future works in "Similarity-based exploded views" ?

3. What is the common strategy for communicating the structure of complex 3D objects?

4. What is the way to preserve the context of volume?

5. What are some of the techniques used to reveal insights into volumetric data?

6. How can the authors get the structured direction of the model?

7. What is the only interaction of the user with the data?

8. What is the main limitation of volume sculpting?

9. What is the entropy rate of a sequence?

10. What is the way to re-organize the parts?

11. What is the viewpoint for evaluating a volume?

12. How do the authors obtain the partitions of the model?

13. How many slabs have been used in the tests?

14. What is the joint probability distribution of X and Y?

15. What is the gain of information when a dataset is divided into two parts?

16. What is the entropy rate of a block of L consecutive symbols?

Figures

Citations

Compact explosion diagrams

Exploded View Diagrams of Mathematical Surfaces

ImNDT: Immersive Workspace for the Analysis of Multidimensional Material Data From Non-Destructive Testing

Hierarchical exploded view generation based on recursive assembly sequence planning

Context-Preserving Volumetric Data Set Exploration Using a 3D Painting Metaphor

References

Elements of information theory

Multimodality image registration by maximization of mutual information

Alignment by Maximization of Mutual Information

The information bottleneck method

Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

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