Due: required posting by May 5th – this is a hard deadline since we need it to be done to do grading.
Canvas Discussion: LINK
It always happens, we get to the end of the semester and there are so many more topics that I want to discuss.
For the last 2 lectures, May 1 and 3, I’ll talk about 3D and Scientific Visualization. This means there won’t be a day to talk about uncertainty (which is a really important topic). So, the required reading will be about uncertainty.
Because the readings are disconnected from the lectures, I’ll let people have extra time to do them / make the required posting. (it is the end of the semester and I know how it goes). Also, there will be no expectation of discussion (since everyone will be busy with their DC3 and other classes) – but please discuss since that’s a better way for learning.
Since 3D and SciVis are interesting to a lot of people, there are optional readings. They are both big topics: a reading or two won’t make a dent. Even finding decent “getting starting” readings is hard.
I recommend that everyone watches the 3 minute video about Visualizing Mummies at the Brittish Museum (video) to motivate why you may care about traditional 3D Vis and SciVis. The video isn’t great, but you’ll get the idea. The paper is pretty cool.
The Readings – Uncertainty
Don’t worry – you don’t have to read all 6!
The first paper is short (it’s an extended abstract), but it gets at a lot of the issues (in an unexpected way).
1. Boukhelifa, N., & Duke, D. J. (2009). Uncertainty visualization: why might it fail? In Proceedings of the 27th international conference extended abstracts on Human factors in computing systems – CHI EA ’09 (p. 4051). New York, New York, USA: ACM Press. doi:10.1145/1520340.1520616 (ACM) (PDF in Box)
In contrast, this is a thorough survey – too much for me to ask everyone to read, but it has a nice diversity.
2. Ken Brodlie, Osorio, R. A., & Lopes, A. (2012). Expanding the Frontiers of Visual Analytics and Visualization. In J. Dill, R. Earnshaw, D. Kasik, J. Vince, & P. C. Wong (Eds.), Expanding the Frontiers of Visual Analytics and Visualization (pp. 81–109). London: Springer London. doi:10.1007/978-1-4471-2804-5 (Springer) (PDF in Box)
I like this next paper because it gets at a variety of different ways to show uncertainty, and points at some of the different strategies. The evaluation aspect is less important for class.
3. MacEachren, A. M., Roth, R. E., O’Brien, J., Li, B., Swingley, D., & Gahegan, M. (2012). Visual Semiotics & Uncertainty Visualization: An Empirical Study. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2496–2505. doi:10.1109/TVCG.2012.279 (PDF)
This one focuses on a single kind of visual technique, but goes a little deeper…
4. Wood, J., Isenberg, P., Isenberg, T., Dykes, J., Boukhelifa, N., & Slingsby, A. (2012). Sketchy Rendering for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2749–2758. doi:10.1109/TVCG.2012.262 (web)
We wrote a paper that deals with a very common case of uncertainty visualization, and one of the most standard visualizations.
5. Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. doi:10.1109/TVCG.2014.2346298 (web)
The statisticians have a lot to say about how we should think about uncertainty, especially in experiments. This paper gets at many of the issues (it is statisticians explaining to psychologists what they should do).
6. Cumming, G., & Finch, S. (n.d.). Inference by eye: confidence intervals and how to read pictures of data. The American Psychologist, 60(2), 170–80. doi:10.1037/0003-066X.60.2.170 (pdf)
What you need to read…
Everyone must read #1 and/or #2 (I recommend both). Each person should read 3 or 4 – but don’t worry about the details of the experiments. (hopefully within each discussion group, there will be a mix). Everyone should look at 5 (but again, get the gist, don’t worry about the details of the experiments). 6 is optional.
For your initial posting, give a sense of the kinds of challenges in visualizing uncertain data. And then describe how the methods in the technique paper you read (3,4) address these. Given that this is the last week of class (and technically, class ends), our expectations are lower. If there is discussion great. But mainly, we want to know that you looked at the readings.
Stuff that is (sadly) optional…
These are topics I really wanted to get to. But there isn’t enough time.
3D
(Note: this is optional. I find it a fascinating topic, and it’s what drew me to vis. We’ll try to cover the high points in class – or as much as we can in 1 lecture).
We’ve been avoiding 3D for most of class. We can’t do it forever. While using 3D for visualization has its problems, sometimes its important (if we’re trying to show 3D phenomena), and sometimes it can be useful.
The initial readings will give you a sense of how we see 3D. The focus is on the perception part. What cues do we use? What can we or can’t we measure visually?
We don’t really have much time in class to discuss how to make pictures that best help people see depth. I have some readings, but we won’t get to them. They are listed below in case you are interested.
Recommended Readings (required in previous years):
- Chapter 5 of Ware’s Visual Thinking for Design (this covers more than 3D, but the non-3D parts are short).
- James Todd. The Visual Perception of 3D Shape. Trends in Cognitive Science. 2004. A nice, compact article. It focuses on a few cues.
artists have dealt with the problem of trying to convey depth in a picture since, well, I’ll let an art historian answer that, but let’s just say a long time. Painters and illustrators have all kinds of tricks. Photographers and filmmakers use light and camera position and other things. Computer Scientists have tried to pick up some of those tricks and systematize them.
This is a chapter of the “Guild Handbook of Illustration” that helps illustrators learn to convey 3D shape in their drawings. A lot of it is about how to think about how light helps you perceive shape (and it does so with fabulous examples). When they start talking about the actual techniques (like how to use charcoal to make the pictures), it’s a little less interesting.
- Light on Form (Chapter 4 of the Guild Handbook of Illustration) by Jessup and Mascaro. (CS protected reader) (box)
Some things that apply well to Vis:
- Amy and Bruce Gooch. Using Non-Photorealistic Rendering to Communicate Shape. SIGGRAPH ‘99 course notes here. (this is better than the original, but seminal paper. you don’t need to read it in detail – just skim through the motivation and look at the pictures).
- Cipriano and Gleicher. Molecular Surface Abstraction.
- Look at the light collages web page (but it links to the initial version of the paper – if you want to read more, check below).
I really wanted to add a few things that show how to effectively use the cues in visualization. But this is just so huge and broad that I don’t know where to start. I’ll mention some of my favorites (some of these are seminal pieces, where there is lots of follow on. some of these are:
- Lee, et al. Geometry-Dependent Lighting. IEEE Trans of Vis and Comp Graphics. (ieee official version). Note: this paper is the extended version of the original Light Collages paper.
- SIGGRAPH 2008 Course notes “Line Drawings from 3D Models” http://www.cs.princeton.edu/gfx/proj/sg08lines/ – These are nice slides that summarize the topic very well.
- DeCarlo, et al. Suggestive Contours for Conveying Shape. Proc. SIGGRAPH 2003. (pdf) (project). The 2003 paper is really seminal, the web page lists some of the follow-ons.
- Linedemann and Ropinski. About the Influence of Illumination Models on Image Comprehension in Direct Volume Rendering. IEEE Vis 2011. (page here)
SciVis
(Note: this is optional. For people who have the problem, it’s a big topic. We’ll try to cover the high points in class – or as much as we can in 1 lecture).
The term “scientific visualization” is somewhat problematic. In some sense, it means what a specific branch of the visualization community likes to call scientific visualization. But, usually it involves visualization of physical (usually spatial) phenomena – that are not cartographic.
The main part of scientific visualization is the visualization of “standard” spatial data types (scalar fields, vector fields, …).
There is a huge literature on how to solve these problems, and they continue to be an active research area.
Volume visualization (3D scalar fields) is the main topic. Arguably, you need to understand it before anything else. It is the mainstay of medical imaging as well.
Some places to get started:
- Munzner Chapter 8 – a basic survey of how to show spatial data
- Arie Kaufman and Klaus Mueller. Overview of Volume Rendering. Chapter 7 of The Visualization Handbook (Hansen and Johnson eds), Academic Press, 2005. (available via the library, but we grabbed it and put it into the reader here, or the author has a preprint here)
- As I mentioned above, the work on 3D Visualization of Mummies at Museums is a good way to motivate advanced volume visualization. (paper)