The schedule for the mini-Workshop on Computational AstroStatistics is set: http://hea-www.harvard.edu/AstroStat/CAS2010/#schedule
Archive for the ‘Imaging’ Category.
I decide to discuss Kalman Filter a while ago for the slog after finding out that this popular methodology is rather underrepresented in astronomy. However, it is not completely missing from ADS. I see that the fulltext search and all bibliographic source search shows more results. Their use of Kalman filter, though, looked similar to the usage of “genetic algorithms” or “Bayes theorem.” Probably, the broad notion of Kalman filter makes it difficult my finding Kalman Filter applications by its name in astronomy since often wheels are reinvented (algorithms under different names have the same objective). Continue reading ‘[MADS] Kalman Filter’ »
Thanks to a Korean solar physicist I was able to gather the following websites and some relevant information on Space Weather Forecast in action, not limited to literature nor toy data.
- Space Weather Research Lab at NJIT
- SEEDS — Solar Eruptive Event Detection System at George Mason University.
- CACTUS A software package for ‘Computer Aided CME Tracking
- SRON in the Netherlands
- I must acknowledge him for his kindness and patience. He was my wikipedia to questions while I was studying the Sun.[↩]
Soon it’ll not be qualified for [MADS] because I saw some abstracts with the phrase, compressed sensing from arxiv.org. Nonetheless, there’s one publication within refereed articles from ADS, so far.
[arxiv:0906.3662] The Statistical Analysis of fMRI Data by Martin A. Lindquist
Statistical Science, Vol. 23(4), pp. 439-464
This review paper offers some information and guidance of statistical image analysis for fMRI data that can be expanded to astronomical image data. I think that fMRI data contain similar challenges of astronomical images. As Lindquist said, collaboration helps to find shortcuts. I hope that introducing this paper helps further networking and collaboration between statisticians and astronomers.
List of similarities Continue reading ‘[ArXiv] Statistical Analysis of fMRI Data’ »
Kriging is the first thing that one learns from a spatial statistics course. If an astronomer sees its definition and application, almost every astronomer will say, “Oh, I know this! It is like the 2pt correlation function!!” At least this was my first impression when I first met kriging.
There are three distinctive subjects in spatial statistics: geostatistics, lattice data analysis, and spatial point pattern analysis. Because of the resemblance between the spatial distribution of observations in coordinates and the notion of spatially random points, spatial statistics in astronomy has leaned more toward the spatial point pattern analysis than the other subjects. In other fields from immunology to forestry to geology whose data are associated spatial coordinates of underlying geometric structures or whose data were sampled from lattices, observations depend on these spatial structures and scientists enjoy various applications from geostatistics and lattice data analysis. Particularly, kriging is the fundamental notion in geostatistics whose application is found many fields. Continue reading ‘[MADS] Kriging’ »
Approximately for a decade, there have been journals dedicated to bioinformatics. On the other hand, there is none in astronomy although astronomers have a long history of comprising a huge volume of catalogs and data archives. Prof. Bickel’s comment during his plenary lecture at the IMS-APRM particularly on sparse matrix and philosophical issues on choosing principal components led me to wonder why astronomers do not discuss astroinformatics. Continue reading ‘Astroinformatics’ »
A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution
Vonesch and Unser (2008)
IEEE Trans. Image Proc. vol. 17(4), pp. 539-549
Quoting the authors, I also like to say that the recovery of the original image from the observed is an ill-posed problem. They traced the efforts of wavelet regularization in deconvolution back to a few relatively recent publications by astronomers. Therefore, I guess the topic and algorithm of this paper could drag some attentions from astronomers. Continue reading ‘Wavelet-regularized image deconvolution’ »
Among billion objects in our Galaxy, outside the Earth, our Sun drags most attention from astronomers. These astronomers go by solar physicists, who enjoy the most abundant data including 400 year long sunspot counts. Their joy is not only originated from the fascinating, active, and unpredictable characteristics of the Sun but also attributed to its influence on our daily lives. Related to the latter, sometimes studying the conditions on the Sun is called space weather forecast. Continue reading ‘space weather’ »
One of [ArXiv] papers from yesterday whose title might drag lots of attentions from astronomers. Furthermore, it’s a short paper.
[arxiv:math.CO:0905.0483] by Harmany, Marcia, and Willet.
Continue reading ‘[ArXiv] Sparse Poisson Intensity Reconstruction Algorithms’ »
I’ve been complaining about how one can do machine learning on solar images without a training set? (see my comment at the big picture). On the other hand, I’m also aware of challenges in astronomy that data (images) cannot be transformed freely and be fed into standard machine learning algorithms. Tailoring data pipelining, cleaning, and processing to currently existing vision algorithms may not be achievable. The hope of automatizing the detection/identification procedure of interesting features (e.g. flares and loops) and forecasting events on the surface of the Sun is only a dream. Even though the level of image data stream is that of tsunami, we might have to depend on human eyes to comb out interesting features on the Sun until the new paradigm of automatized feature identification algorithms based on a single image i.e. without a training set. The good news is that human eyes have done a superb job! Continue reading ‘An excerpt from …’ »
Astronomy is known for its pretty pictures, but as Joe the Astronomer would say, those pretty pictures don’t make themselves. A lot of thought goes into maximizing scientific content while conveying just the right information, all discernible at a single glance. So the hardworkin folks at Chandra want your help in figuring out what works and how well, and they have set up a survey at http://astroart.cfa.harvard.edu/. Take the survey, it is both interesting and challenging!
Our hometown rag (the Boston Globe) runs an occasional series of photo collections that highlight news stories called The Big Picture. This week, they take a look at the Sun: http://www.boston.com/bigpicture/2008/10/the_sun.html
The pictures come from space and ground observatories, from SoHO, TRACE, Hinode, STEREO, etc. Goes without saying, the images are stunning, and some are even animated. The real kicker is that images such as these are being acquired by the hundreds, every hour upon the hour, 24/7/365.25 . It is like sipping from a firehose. Nobody can sit there and look at them all, so who knows what we are missing out on. Can statistics help? Can we automate a statistically robust “interestingness” criterion to filter the data stream that humans can then follow up on?
Without signal processing courses, the following equation should be awfully familiar to astronomers of photometry and handling data:
$$c_k=\int_\Lambda l(\lambda) r(\lambda) f_k(\lambda) \alpha(\lambda) d\lambda +n_k$$
Terms are in order, camera response (c_k), light source (l), spectral radiance by l (r), filter (f), sensitivity (α), and noise (n_k), where Λ indicates the range of the spectrum in which the camera is sensitive.
Or simplified to $$c_k=\int_\Lambda \phi_k (\lambda) r(\lambda) d\lambda +n_k$$
where φ denotes the combined illuminant and the spectral sensitivity of the k-th channel, which goes by augmented spectral sensitivity. Well, we can skip spectral radiance r, though. Unfortunately, the sensitivity α has multiple layers, not a simple closed function of λ in astronomical photometry.
Or $$c_k=\Theta r +n$$
Inverting Θ and finding a reconstruction operator such that r=inv(Θ)c_k leads spectral reconstruction although Θ is, in general, not a square matrix. Otherwise, approach from indirect reconstruction. Continue reading ‘[tutorial] multispectral imaging, a case study’ »
At least two images for reconstructing a 3D scene is a conventional belief. Yet, we do know that our eyes reconstruct 3D scenes from various single snap shot images, just with one picture. Based on our perception and learning ability or our internal pattern recognition ability, a few groups of people have been trying to reconstruct a 3D image from one still image picture. Luckily you can test such progress, reconstructing a 3D scene from a single still image at Make3D (a click brings you to Make3D at Stanford). Continue reading ‘Make3D’ »