Can any one please provide me the Source Code for "ScatterPlotDemo1" which comes with "JfreeChart DevloperGuide".I am developing exactly the same Application so it would be very helpful for me if I can get the code for the following Image attached.
Thanks very Much
http://www.jfree.org/jfreechart/images/ScatterPlotDemo1.png
Any one of these recent scatter plot demos would probably be a better starting point. The only difference is the demonstration dataset. ScatterPlotDemo1 is not hard to find, but the required SampleXYDataset2 is less than exemplary. I'd look at nextGaussian() to vary the slope of a line.
As you use the library frequently, I'd recommend The JFreeChart Developer Guide†.
†Disclaimer: Not affiliated with Object Refinery Limited; just a satisfied customer and very minor contributor.
http://code.google.com/p/uidesign/source/browse/trunk/UIDProject/src/testers/ScatterPlotDemo1.java?spec=svn19&r=19
Related
I came across this page: Plotting Tools
where I found a set of tools with the name stats.pl and make-cdf. I can write my own but don't want to spend too much time when someone else has already done that. Does anyone have these tools or at least point me to a similar set of tools somewhere?
I do not know who Dave, Binju, Vijay and Dan are and I did not see a way of figuring out what stats.pl and make-cdf contain.
There are a number of excellent statistics related modules on CPAN including Statistics::Descriptive, Statistics::KernelEstimation and Math::GSL::CDF to name but a few that might be relevant given the names of the scripts you mention.
However, if you want to do serious statistics, I would recommend you consider using R which you can control using Statistics::R. AFAIK, the R tag on StackOverflow is pretty active.
I am working for a project at school regarding face detection, based on a technique described by Viola and Jones 2001/2004.
I've read that the OpenCV has an implementation of this algorithm, and it works very good.
I was wondering if you have any advices regarding what techniques (pre-processing) to apply to the images before testing the existence of a face (eg. histogram equalization) ?
I basically used the code from this sample program from the OpenCV page and it worked very well for my masters thesis project. If you get bad results or your lighting is strange you can try a histogram equalization.
with a friend I did something similar too for an university project, and especially on low resolution video sequences it really helped to upsample the frame, doubling its size. It was my friends' idea, who had previously taken an image processing class. Although equivalent, things like decreasing initial scan window size, horizontal and vertical steps didn't produce the same result. In other words it may be better to work on larger images with larger scan windows than on smaller with smaller scan windows. Don't know exactly why.
Bye ;-)
I know its too late. But do go through this site as well.
It speaks of the common pre-proccessing required for the images. Equalising the image, Editing irrelevant content etc
Is there anything other than DDD that will draw diagrams of my data structures like DDD does that runs on Linux?
ddd is okay and runs, just kind of has an old klunky feeling to it, just wanted to explore alternatives if there are any.
The top part with the grid of this image is what I am talking about:
Don't you mind to look here (list of GDB front-ends)?
I suggest this list should be useful.
I've used zero bugs a few times. It can do custom visualization. I don't know if allows the users to effect the gui elements or just how it displays in the text listings. Check it out, www.zero-bugs.com.
For those that wanted an answer; you are looking for KDBG.
ZeroBugs data visualizations can be customized via Python scripts. The debugger is now available as open source (and free as in free beer, it can be used for commercial purposes). Check it out at http://zerobugs.codeplex.com/
I am thinking about creating a database system for images where they are stored with compact signatures and then matched against a "query image" that could be a resized, cropped, brightened, rotated or a flipped version of the stored one. Note that I am not talking about image similarity algorithms but rather strictly about duplicate detection. This would make things a lot simpler. The system wouldn't care if two images have an elephant on them, it would only be important to detect if the two images are in fact the same image.
Histogram comparisons simply won't work for cropped query images. The only viable way to go I see is shape/edge detection. Images would first be somehow discretized, every pixel being converted to an 8-level grayscale for example. The discretized image will contain vast regions in the same colour which would help indicate shapes. These shapes then could be described with coefficients and their relative position could be remembered. Compact signatures would be produced out of that. This process will be carried out over each image being stored and over each query image when a comparison has to be performed. Does that sound like an efficient and realisable algorithm? To illustrate this idea:
removed dead ImageShack link
I know this is an immature research area, I have read Wikipedia on the subject and I would ask you to propose your ideas about such an algorithm.
SURF should do its job.
http://en.wikipedia.org/wiki/SURF
It is fast an robust, it is invariant on rotations and scaling and also on blure and contrast/lightning (but not so strongly).
There is example of automatic panorama stitching.
Check article on SIFT first
http://en.wikipedia.org/wiki/Scale-invariant_feature_transform
If you want to do a feature detection driven model, you could perhaps take the singular value decomposition of the images (you'd probably have to do a SVD for each color) and use the first few columns of the U and V matrices along with the corresponding singular values to judge how similar the images are.
Very similar to the SVD method is one called principle component analysis which I think will be easier to use to compare between images. The PCA method is pretty close to just taking the SVD and getting rid of the singular values by factoring them into the U and V matrices. If you follow the PCA path, you might also want to look into correspondence analysis. By the way, the PCA method was a common method used in the Netflix Prize for extracting features.
How about converting this python codes to C back?
Check out tineye.com They have a good system that's always improving. I'm sure you can find research papers from them on the subject.
The article you might be referring to on Wikipedia on feature detection.
If you are running on Intel/AMD processor, you could use the Intel Integrated Performance Primitives to get access to a library of image processing functions. Or beyond that, there is the OpenCV project, again another library of image processing functions for you. The advantage of a using library is that you can try various algorithms, already implemented, to see what will work for your situation.
I run a website that allows users to write blog-post, I would really like to summarize the written content and use it to fill the <meta name="description".../>-tag for example.
What methods can I employ to automatically summarize/describe the contents of user generated content?
Are there any (preferably free) methods out there that have solved this problem?
(I've seen other websites just copy the first 100 or so words but this strikes me as a sub-optimal solution.)
Think of the task of summarization as a challenge to 'select the most important sentences' from the document.
The method described in The Automatic Creation of Literature Abstracts by H.P. Luhn (1958) describes a naive method that actually performs quite well. Try giving it a shot.
If your website is in Python coding this algorithm using the NLTK (Natural Language Toolkit) is a fun task.
Make it predictable.
From a users perspective simply using the first paragraph is not bad at all.
Using any automation is bound to fall flat in some cases. So I suggest to display
the first paragraph (maybe truncating at some point) as a summary and offer the ability to override that by an optional field.
I might try using mechanical Turk or any number of other crowdsourcing options.
Another item to check out, a SourceForge project, AutoSummary Semantic Analysis Engine
Not a trivial task... You should look for articles or books on "extractive summarization"
A few starters could be:
Books:
Natural Language Processing with Python
Foundations of Statistical Natural Language Processing
Articles:
Language independent extractive summarization
Extractive summarization: how to identify the gist of a text
Extractive Summarization using Inter- and Intra- Event Relevance
Yahoo has a free API for this:
http://developer.yahoo.com/search/content/V1/termExtraction.html
Apple's patent 6424362 - Auto-summary of document content contains sample code which might be useful...
This borders on artificial intelligence so there's not going to be an "easy" solution out there, but there are products that target this problem.
Check out Copernic Summarizer, for one.
Noun phrases typically tend to be important elements of a sentence. Picking sentence(s) with a high density of noun phrases could yield a good summary. You could get noun phrases using a POS tagger.
For a good summary, it is desirable that it is a meaningful sentence. Reading a broken sentence is slightly jarring.
Alternatively, when the author posts the article, the author can highlight what are the keywords that can be used in the description which can then be automatically put in the meta description tag.