An article close to my current work on 3D now:
D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47(1/2/3), pp. 7-42, April-June 2002.
In their article, Scharstein and Szeliski make a comparison of stereo estimation algorithms. But they do not just offer this overview of algorithms. On their webpage, they also provide the source code, and a widely used dataset of stereo images. They also invite other researchers to try their own algorithm on this dataset, and upload the results. This has resulted over the years in a performance comparison of almost 50 stereo algorithms, nicely listed on their webpage.
A nice example of what reproducible research can do! I think we need a lot more of these comparisons on common (representative) datasets.
I just read the following article:
C. Laine, S. N. Goodman, M. E. Griswold, and H. C. Sox, Reproducible Research: Moving toward Research the Public Can Really Trust, Annals of Internal Medicine, Vol. 146, Nr. 6, pp. 450-453, 2007.
A very interesting article, about how the journal “Annals of Internal Medicine” is promoting reproducible research. They do not require that all papers are reproducible, but they do ask the authors of each paper whether theirs is reproducible or not. If it is reproducible, they provide links to the protocol, data, or statistical code that was used.
While, certainly in medicine, this still does not guarantee that the entire research work is reproducible, it does give a lot of additional information (and credibility) about the presented work. I (as an ignorant researcher) also found it very interesting to read the description of the thorough editorial process that each paper undergoes. I have put an overview of reproducible research initiatives by journals on our RR links page. That is, the initiatives I know about of course. Feel free to let me know if you know other examples!
This initiative was (among others) initiated by an article about this topic by Peng et al. It would be great if other journals take over these examples, and reproducible research becomes the ‘default’ for a paper…
For years, camera manufacturers have been building cameras with more and more pixels. It seems/seemed as if there is no limit to the number of megapixels you can get in your camera.
In theory, this is true: the more pixels you have in your camera, the more details you can capture, and the larger the prints you can make. However, we also all want small, compact cameras. So the camera manufacturers do their best to fit this larger number of pixels into the same or an even smaller camera housing, with the same sensor and lens size. And that’s where it gets tricky, because if you increase the number of pixels and keep the sensor size the same, the size of each pixel has to decrease. And a smaller pixel can capture less light, which results in relatively more noise. So the resulting images are noisier. Also, it does not make much sense to have a sensor that can capture more details if your lens does not let those details pass through. The lens should therefore also be replaced by one with a higher quality, and better lenses are typically also bigger.
So, there is an optimum. And the people at Image Engineering have measured that the optimum for a compact camera is 6 megapixels. They have even devoted a website to it. Of course, this does not hold if you also increase the sensor size (for example in SLR cameras). It is therefore better to say that the optimal pixel size is 3 micrometer. Depending on the sensor size, one can then compute the optimal number of pixels.