In an effort to improve the image quality I'm getting from a camera I started digging up alternative demosaicing technics. I found out that there are allot of different algorithms but most of them are dated back to 2000. Since more than a decade has passed, I was wondering what are the latest technics around?
Algorithm or source code would be highly appreciated.
It's not the most recent work, but the results are a good improvement over classic algorithms: http://www.ipol.im/pub/algo/bcms_self_similarity_driven_demosaicking/
And you can explore the site for more algorithms and examples, with online demos and source code (open source licences). It should help you getting started.
Not latest, published in 2007, the demosaicing with DFPD (Directional Filtering and a posteriori Decision) seems to give the best result. Not only it removes the artifacts at high frequency regions, it produces more crisp and natural colors than previous approaches.
This blog post compares the DFPD approach with hq bilinear interpolation and the result is quite obvious: DFPD reconstructs the image with better colors and without artifacts.
Related
Problem:
Atmospheric turbulation problem occurs due to foze,temperature variations, looking at to long distances with camera. There is a problem sample below
Example:
What I tried so far:
When I searched, I found this article which suggests to use
Dual Tree Complex Wavelet Transform(DTCWT). I followed through the
article and it seems like a good way to apply. Then I found this
implementation of the article in python code. I have run it and
seen the results but not as expected well.
I have simply took the average of some frames(let say 10), and get a result. I did it because the shaking pixels will be mostly true ones at any time. My results seems not bad after averaging.
What I need:
After searching much I am sure that this solution needs more frame to get one good frame. To do that I need a good image fusion algorithm.
I kindly request that you can suggest me any algorithm or any approaches I can try and go throuh to solve problem.
Note: I am familiar with OpenCV and C++ so priority is on these.
I'm trying to implement my own version in MATLAB of the SIFT algorithm. I know that there are a lot of existing implementations but I would like to develop my own version to better understand the whole process.
At the moment I'm able to find the keypoints in the different scale-space and octaves but I didn't understand how to bring back to the original octaves the keypoints that I found in the other octaves. Is it done with a simple interpolation?
I read the original article of Lowe and also other tutorials but I miss this point. Any suggestion is greatly appreciated!
Can someone explain briefly how SDM (Supervised Descent Method) for Feature Extraction works?
I searched a lot on the Internet but couldn't found what I was looking for.
Is it only for feature extraction in videos, or can it be used in both videos and images?
If someone can explain, it would be of great help.
SDM is a method to align shapes in images. It uses feature extractors (SIFT and HoG) in the process, but is not a feature extractor.
Similar methods are ASM, AAM or CLM, but SDM has better performance and accuracy.
In the case of SDM, in the training process, the system learns some descent vectors from a initial shape configuration (different from the shapes in the database) to the database sets. Those vectors have the hability of fitting a new initial configuration with the face shape in the image you want to fit in.
This link can help you to learn more about it: http://arxiv.org/pdf/1405.0601v1.pdf
About the code, there is some demo samples in the main page of IntraFace but if you are looking for the code, I don´t think you can find it.
You can use vl_sift for beginning its working even more precise then their original descriptor however its not fast as their descriptor for real time implementation.
Regarding to their implementation no code were released so far. They are using some specialized version with very fast histogram calculation.
I need to implement algorithm which for input has picture ( jpeg ) and create new picture like output, but only with bodies ( background is removed completely ). Input picture is picture with people from vacation and I need to recognize human bodies and remove background. Can someone suggest me what algorithm to use, what book to buy to learn that algorihms ?
Check this link it will perfectly answer your question of removing the background and performing further processing
neural networks are particuarly useful for this kind of task, but the theory is a universe, if you're doing it from scratch ... that's a lot of work
This is a segmentation problem. In the general case, segmenting images is a hard research problem (I just spent five years doing a doctorate on segmenting greyscale medical images, for example) and the way you go about it is strongly tied to the type of images with which you have to deal. The best advice I can give is to go and read the appropriate literature on segmenting colour images (e.g. use Google Scholar). In terms of books, this one's a good general-purpose introduction to image processing:
http://www.amazon.co.uk/Digital-Image-Processing-Rafael-Gonzalez/dp/0130946508/ref=sr_1_7?ie=UTF8&qid=1326236038&sr=8-7
Searching for "segmenting people in colour images" on Google seems to turn up some good links, incidentally.
I have a question for you: you want to implement this using an algorithm? If so, then it might require a lot of things to be done (provided you are new to the field of image processing).
Otherwise you may try using masking techniques in image editing software like Adobe Photoshop (that would hardly take 15 mins, depending upon how well you know it)
A good book to start with image processing techniques is: "Digital Image Processing" by Gonzalez and Woods; it starts from the basics, and explains stuff in depth.
Still it may take a lot of time to develop an algorithm to do this job. I recommend you use some library for the same. OpenCV(opensource computer vision) is an excellent choice. The library itself comes with demos which include programs for face detection etc. The inbuilt functions provide a variety of features (edge detection/Feature identification and extraction, you may have to use this) Here's the link
http://opencv.willowgarage.com/wiki/
The link provides a lot of reference material that you can make use of! :)
Start with facial recognition software and algorithms; they have been the most refined over the years and as long as all of your bodies have heads, you can use exif data to figure image capture orientation (of course you can't completely rely on that), sample the facial skin to get skin tone ranges, and find the attached body. Anything that is not head and body should be deleted. This process assumes that a person has roughly the same skin tone on their face as their body and the camera flash isn't washing this out. You could grab the flash duration and some other attributes from exif and adjust your ranges accordingly.
A lot of software out there can recognize faces (look at iPhoto for example), so you'll have to use the face as a reference point, along with skin tone, to find your body edges. You result isn't going to be perfect, but as long as your approach is sound, you'll end up with something useful.
And release your software as open source when you're done so I can use it... :)
You can download a free PDF of the book Computer Vision by Richard Szeliski from the author's website. Not only do you have a free book on algorithms, but it's a book that addresses this specific problem.
http://szeliski.org/Book/
You'll see this image at the top of that page of the author's website.
Used copies of the hardcover are available for about $62 if you check addall.com. If you spent some time doing image processing, you'll appreciate having a paper copy of at least one good general reference book.
Its tough but not impossible. I can't give you any code but Peter Norvig had a great talk on the value of data and in the talk he shows how he was able to take a picture of lake and remove all the houses blocking the image and have the lake expanded with boats,etc..
The computer basically learned how lakes look and boats go on lakes and then removed the houses and placed it there. He explains his process(but no code or anything).
Here it is:
Peter Norvig - The Unreasonable Effectiveness of Data
http://www.youtube.com/watch?v=yvDCzhbjYWs
I'm building a rhythm-based game, and facing a lot of problems with rhythm-detection. I receive the current spectrum of a playing song. It looks like a float array with 512 floats. 256 for left and right channel representation. FFT is also available. But I have no idea how to work with that data, I've made some experiments with visualizing, but it gave me very few information. I've googled for some ready algorithms, but there is nothing. Please, can someone help me with, maybe, some references, materials, articles connected with rhythm detection, working with audio spectrum. Code will also be very helpful. Thanks.
Maybe you didn't use the right search-terms. Try to google 'tempo detection' or 'beat detection', together with 'code' or 'algorithm'. There are lots of papers, references, code examples, etc.
Just a few hits:
http://www.cs.princeton.edu/~lieber/cos325/final/
http://www.clear.rice.edu/elec301/Projects01/beat_sync/beatalgo.html
You might want to check out the source and project report for the Dancing Monkeys project. Dancing monkeys automatically generates stepfiles for DDR, and it does so using some rather sophisticated beat detection. It's written in matlab.
You should have a look at the beat spectrum algorithm: http://www.rotorbrain.com/foote/papers/icme2001/icmehtml.htm.
It extracts information about rythm and musical structure by computing the similarity of small samples' spectrograms. It is relatively easy to implement and allows robust information to be retrieved.