InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 2

2009-12-04T12:41:15Z

UE-InfoVis0910 0325190:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]
=== Zu beurteilende Tabelle ===
[[Image:table4.gif]]

===Critics===
The main point of failure is the bad readability of the table. The eye of the reader gets distracted by the grid. The missing vertical white spaces intensify this effect. 
There should be annotations that explain the abbreviations in the header. 
Because of the missing knowledge in this matter, we can't comment on the importance of the number of decimal places. 
According to Stephen Few, numbers should be aligned at the right end of a field.

===New Table===
[[Image:table4new.jpg|600px]]

===Changes===
* changed the grid into bigger white spaces and changed the background of every second row to 10% grey. This enhanced the readability significantly.
* Added annotations to explain the abbreviations to help understanding the table.
* Devided header into header spanner header and spanner rule.
* Changed orientation for numbers to right alignment.

===Redesigned Table===
[[Image:infovis_rechts.jpg|600px]]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15]]

File:Volvis1a.jpg

2009-11-06T12:05:30Z

UE-InfoVis0910 0325190:

== Summary ==
Different layers from a CT scan
== Copyright status ==
Andreas Lenzhofer
== Source ==
Andreas Lenzhofer

File:Volvis2a.jpg

2009-11-06T10:42:36Z

UE-InfoVis0910 0325190:

== Summary ==
Direct Volume Rendering
== Copyright status ==

== Source ==
[Levoy 1988] Marc Levoy, ''Display of Surfaces from Volume Data '', IEEE Computer Graphics and Applications Vol. 8 No. 3, pp. 29-37, May 1988, http://graphics.stanford.edu/papers/volume-cga88/

Teaching talk:TUW - UE InfoVis WS 2005/06 - Gruppe 10 - Aufgabe 1 - Grid Based Layout

2009-11-05T16:32:36Z

UE-InfoVis0910 0325190: New page: *Rechtschreibfehler ausgebessert

*Rechtschreibfehler ausgebessert

File:Volvis1.jpg

2009-11-05T16:18:11Z

UE-InfoVis0910 0325190:

== Summary ==
Different layers from a CT scan
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:15:22Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head. After a projection, we can obtain different kinds of images, depending on what we want to see. 
 
[[{{ns:6}}:volvis1.jpg|200px]] 
Different images from a CT scan 
 
[[{{ns:6}}:volvis2.jpg|200px]] 
Direct Volume Rendering 
 
There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:13:02Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head. After a projection, we can obtain different kinds of images, depending on what we want to see.

[[{{ns:6}}:volvis1.jpg|200px]] 
Different images from a CT scan 

[[{{ns:6}}:volvis2.jpg|200px]]
Direct Volume Rendering

There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:11:10Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head. After a projection, we can obtain different kinds of images, depending on what we want to see.
[[{{ns:6}}:volvis1.jpg|200px]] 
Different images from a CT scan 

[[{{ns:6}}:volvis2.jpg|200px]]

There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:09:13Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head. After a projection, we can obtain different kinds of images, depending on what we want to see.
[[{{ns:6}}:volvis1.jpg|200px]]
Different images from a CT scan
[[{{ns:6}}:volvis2.jpg|200px]]

There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:07:33Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head. After a projection, we can obtain different kinds of images, depending on what we want to see.
[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching talk:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:05:33Z

UE-InfoVis0910 0325190:

* Definitions hinzugefügt: Nützlich um einen ersten Eindruck des Begriffes zu bekommen.
* Bibliography hinzugefügt: Externe Links zu wenig Aussagekräftig. Korrekte Analyse benötigt ein Literaturverzeichnis
* Introduction anstelle von Overview of ... : Thema der Seite ist bereits bekannt und muss nicht noch mehrmals erwähnt werden.
* Rechtschreibung und Grammatikfehler ausgebessert
* External Links entfernt

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-11-05T16:04:57Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different techniques of Volume Visualization consist of projecting 3D dataset onto 2D images. Those techniques are used in many domains such as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a projection, we can obtain different kinds of images, depending on what we want to see. We can see two different ways of the use of Volume Visualization on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are different algorithms for Volume Visualization, the basic steps for all of those algorithms are:

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fundamental algorithms for Volume Visualization:

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. These methods are effective with amorphous features such as clouds, fluids, and gases.These methods have one disadvantage, they need to traverse all the dataset for each rendered image, and each recalculation can be time-consuming. There is a solution to avoid this problem : the "progressive refinement". It consists of creating a low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time-consuming since the original dataset must be traversed again.

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching talk:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T13:16:24Z

UE-InfoVis0910 0325190:

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T13:14:09Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Introduction==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml] 

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching talk:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T13:05:28Z

UE-InfoVis0910 0325190:

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T13:01:50Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", [T. Todd Elvins, 1992]
==Overview of the Volume Visualization==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml] 

==Bibliography==
[Elvins, 1992] T. Todd Elvins. ''Computer Graphics 26:3'', pp. 194-201 (August, 1992)

Teaching talk:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:58:42Z

UE-InfoVis0910 0325190: New page: * Deinitions hinzugefügt: Nützlich um einen ersten Eindruck des Begriffes zu bekommen. * Bibliography hinzugefügt: Externe Links zu wenig Aussagekräftig. Korrekte Analyse benötigt ein...

* Deinitions hinzugefügt: Nützlich um einen ersten Eindruck des Begriffes zu bekommen.
* Bibliography hinzugefügt: Externe Links zu wenig Aussagekräftig. Korrekte Analyse benötigt ein Literaturverzeichnis

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:53:47Z

UE-InfoVis0910 0325190:

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:50:03Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization",[T. Todd Elvins, 1992]
==Overview of the Volume Visualization==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml] 

==Bibliography==
T. Todd Elvins, Computer Graphics 26:3, pp. 194-201 (August, 1992)

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:44:09Z

UE-InfoVis0910 0325190:

==Definitions==
*"A Survey of Algorithms for Volume Visualization", T. Todd Elvins, Computer Graphics 26:3, pp. 194-201 (August, 1992)
==Overview of the Volume Visualization==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml]

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:42:24Z

UE-InfoVis0910 0325190:

==Definitions==
"A Survey of Algorithms for Volume Visualization", T. Todd Elvins, Computer Graphics 26:3, pp. 194-201 (August, 1992)
==Overview of the Volume Visualization==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml]

Teaching:TUW - UE InfoVis WS 2007/08 - Gruppe 03 - Aufgabe 1 - Volume Visualization

2009-10-31T12:39:56Z

UE-InfoVis0910 0325190:

==Definitions==

==Overview of the Volume Visualization==

Volume Visualization is the process of understanding multidimensional dataset by projecting it onto 2D images. Generally, the different Techniques of Volume Visualisation consist on projecting 3D dataset onto 2D images. Those techniques are used in many domains as medicine, geoscience, astrophysics, chemistry, microscopy, mechanical engineering, ...

For example, with a CT scan, we obtain a lot of images which are layers of the head like on the first photo. After a prjoection, we can obtain different kind of images, depending on what we want to see. We can see two different way of the use of volume visualisation on the second photo.

[[{{ns:6}}:volvis1.jpg|200px]] [[{{ns:6}}:volvis2.jpg|200px]]

There are differents algorithms for Volume Visualization, the basic steps for all of those algorithms are as follows :

1. Data acquisition either via empirical measurement or computer simulation.
2. Put the data into a format that can be easily manipulated.
3. The data is mapped onto geometric or display primitives.
4. The primitives are stored, manipulated, and displayed.

==The different methods for Volume Visualization==

There are two fondamental algorithms for Volume Visualization :

1. Direct volume rendering (DVR) algorithms.
2. Surface-fitting (SF) algorithms.

===Direct Volume Rendering===

DVR methods map elements directly into screen space without using geometric primitives as an intermediate representation. This methods is effective with amorphous features such as clouds, fluids, and gases.There a disadvantage with this methods, we need to traverse the all the dataset for each rendered image, and it can be long to recalculate each time. There is a solution to avoid this problem : the "progressive refinement". it consists on creating low resolution image and then refining it by increasing the resolution and the quality.

===Surface-Fitting===

SF methods are also called feature-extraction or iso-surfacing and fit planar polygons or surface patches to constant-value contour surfaces. SF methods are usually faster than DVR methods since they traverse the dataset once, for a given threshold value, to obtain the surface and then conventional rendering methods (which may be in hardware) are used to produce the images. New views of the surface can be quickly generated. Using a new threshold is time consuming since the original dataset must be traversed again.

==External links==

[http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm http://www.siggraph.org/education/materials/HyperVis/vistech/volume/volume.htm] 
[http://www.andrewwinter.com/visualization/introduction/ http://www.andrewwinter.com/visualization/introduction/] 
[http://www.cg.tuwien.ac.at/research/vis/vismed/MM/ http://www.cg.tuwien.ac.at/research/vis/vismed/MM/] 
[http://www.llnl.gov/icc/sdd/img/multiresolution.shtml http://www.llnl.gov/icc/sdd/img/multiresolution.shtml]

User:UE-InfoVis0910 0325190

2009-10-27T09:37:11Z

UE-InfoVis0910 0325190:

===Name:===
MARTIN, Markus

[[Image:Metallica.jpg]]

User:UE-InfoVis0910 0325190

2009-10-21T16:54:48Z

UE-InfoVis0910 0325190:

===Name===
MARTIN, Markus
===Matrikelnummer===
0325190

[[Image:Metallica.jpg]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15

2009-10-21T16:52:13Z

UE-InfoVis0910 0325190:

== Gruppenmitglieder ==
[[User:UE-InfoVis0910_0325190|Martin, Markus]] 
[[User:UE-InfoVis0910_0425751|Stix, Harald]] 
[[User:UE-InfoVis0910_0300665|Lenzhofer, Andreas]] 

== Aufgaben ==
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 3|Aufgabe 3]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 4|Aufgabe 4]]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15

2009-10-21T16:51:51Z

UE-InfoVis0910 0325190:

== Gruppenmitglieder ==
[[User:UE-InfoVis0910_0325190|?, Markus]] 
[[User:UE-InfoVis0910_0425751|Stix, Harald]] 
[[User:UE-InfoVis0910_0300665|Lenzhofer, Andreas]] 

== Aufgaben ==
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 3|Aufgabe 3]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 4|Aufgabe 4]]

Teaching:TUW - UE InfoVis WS 2009/10

2009-10-21T16:50:13Z

UE-InfoVis0910 0325190:

[[Image:Aigner03infovis ue.gif]] <big>WS 2009/10</big>

'''LVA Nr:''' 188.308 ([http://tuwis.tuwien.ac.at/lva/tuwien/188308 TUWIS++ Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/index.html

'''Leitung:''' [[Gschwandtner, Theresia|Theresia Gschwandtner]] [gschwandtner (at) ifs.tuwien.ac.at] 

== Gruppen ==


[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 02|Gruppe 02 (Feichtinger, Rezaei, Schindelka)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 03|Gruppe 03 (Lang, Hackl, Hasslacher)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04 (Kaiser, <NACHNAME>, Ehsani)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 05|Gruppe 05 (Paizoni, Wuttej, Hudl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 06|Gruppe 06 (Fried, Fritz, Hiller)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09|Gruppe 09 (Hubmann-Haidvogel, Kloibhofer, Riederer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 13|Gruppe 13 (Sadauskas, Scheikl, <NACHNAME3>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 14|Gruppe 14 (Gastecker, Hahn, Leeb)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15 (Martin, Stix, Lenzhofer)]]

== News / Bemerkungen ==

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnamen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:05, 01 October 2009 (CEST)

Teaching:TUW - UE InfoVis WS 2009/10

2009-10-21T16:49:33Z

UE-InfoVis0910 0325190:

[[Image:Aigner03infovis ue.gif]] <big>WS 2009/10</big>

'''LVA Nr:''' 188.308 ([http://tuwis.tuwien.ac.at/lva/tuwien/188308 TUWIS++ Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/index.html

'''Leitung:''' [[Gschwandtner, Theresia|Theresia Gschwandtner]] [gschwandtner (at) ifs.tuwien.ac.at] 

== Gruppen ==


[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 02|Gruppe 02 (Feichtinger, Rezaei, Schindelka)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 03|Gruppe 03 (Lang, Hackl, Hasslacher)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04 (Kaiser, <NACHNAME>, Ehsani)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 05|Gruppe 05 (Paizoni, Wuttej, Hudl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 06|Gruppe 06 (Fried, Fritz, Hiller)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09|Gruppe 09 (Hubmann-Haidvogel, Kloibhofer, Riederer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 13|Gruppe 13 (Sadauskas, Scheikl, <NACHNAME3>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 14|Gruppe 14 (Gastecker, Hahn, Leeb)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15 (?, Stix, Lenzhofer)]]

== News / Bemerkungen ==

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnamen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:05, 01 October 2009 (CEST)

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15 - Aufgabe 0

2009-10-20T17:07:54Z

UE-InfoVis0910 0325190: New page: =Aufgabe 0: Wiki Basics= '''Punkte:''' 5 '''Abgabe:''' spätestens bis zum 28.10.2009 über das InfoVis:Wiki ==Ziele== * Erlernen von Grundkenntnissen von Wiki Systemen * Vertrautm...

=Aufgabe 0: Wiki Basics=
'''Punkte:''' 5 
'''Abgabe:''' spätestens bis zum 28.10.2009 über das InfoVis:Wiki

==Ziele==

* Erlernen von Grundkenntnissen von Wiki Systemen
* Vertrautmachen mit dem InfoVis:Wiki
* Bildung von 3er-Gruppen und Schaffung der Basis für die weiteren Übungsaufgaben

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15

2009-10-13T16:33:20Z

UE-InfoVis0910 0325190: New page: <Martin>, <Markus>

[[User:UE-InfoVis0910_<0325190>|<Martin>, <Markus>]]

User:UE-InfoVis0910 0325190

2009-10-13T16:28:20Z

UE-InfoVis0910 0325190:

MARTIN, Markus 
Matrikelnr.: 0325190 
[[Image:Metallica.jpg]]

User:UE-InfoVis0910 0325190

2009-10-13T16:25:43Z

UE-InfoVis0910 0325190:

MARTIN,Markus 
[[Image:Metallica.jpg]]

User:UE-InfoVis0910 0325190

2009-10-13T16:21:37Z

UE-InfoVis0910 0325190:

MARTIN,Markus
[[Image:Metallica.jpg]]

User:UE-InfoVis0910 0325190

2009-10-13T16:19:35Z

UE-InfoVis0910 0325190:

MARTIN,Markus
[[Image:Userimage_0325190.jpg]]

File:Metallica.jpg

2009-10-13T16:18:42Z

UE-InfoVis0910 0325190: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

User:UE-InfoVis0910 0325190

2009-10-13T16:06:46Z

UE-InfoVis0910 0325190: New page: MARTIN,Markus

MARTIN,Markus