I was using adobe photoshop and I was amazed by the "quick selection tool" they have. By just clicking on the object and moving the mouse a little I was able to select an object inside the image very quickly. I was wondering how they have achieved that ? (Doesn't look like artificial neural network. Maybe some edge detection algorithm) I have search over net over the possibilities of the algorithms that could have been used and came around Intelligent Scissors for Image Composition but it have already been implemented in GIMP and this tool is no where close to adobes. Maybe they are using combination of Interactive Segmentation Algorithms and Intelligent scissor.
Maybe someone here could point me to some important thread or research paper pointing to this area.
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I'm working on an educational project where I need to detect objects, mainly doors and windows. I have tried to find specific algorithms to do this.
This is the first step in a project to detect all objects and let a user choose the object he wants. Then in the next step the system will define edges of the object accurately.
I want to detect objects by their color variety with background or with overlapping objects. I need an algorithm to start with. I started learning color spaces and I chose hvs color space. I read many papers and I know how they work, but I'm still confused and don't know what algorithm will really help.
You can use any segmentation algorithm.
You will need to find features from images to use in segmentation, a good approach for feature selection is by using any deep learning technique, i would recommend try CNN, you will find a builtin library "matconvnet" "http://www.vlfeat.org/matconvnet/" for implementing CNN in MATLAB.
you can also find few already build models for segmentation using CNN here http://www.vlfeat.org/matconvnet/pretrained/
I do not have a background in image recognition/feature extraction, but I am in need of a way to extract trees from an image without the background vegetation.
Seen above is a small example of the kind of imagery I'm working with. I have access to multi-spectral imagery as well (though I haven't seen it yet) including NDVI, NIR, Red-edge.
From researching the problem at hand, I am aware that feature extraction is an active area of research and it seems that often supervised and unsupervised machine learning is employed in combination with statistical voodoo such as "PCA". Being able to differentiate between trees and background vegetation has been noted as an area of difficulty in some papers I skimmed over in my research.
There are notable features about the imagery I am working with. First of all, the palm trees have a very distinctive shape. Not only this, but there are obvious differences in the texture of the trees vs the texture of the background vegetation.
I am not an academic, and as such I only have access to publicly available papers for my research. I am looking for relevant algorithms that could help me extract the features of interest to me (trees) that either have an implementation (ideally in C or bindings to C, though I'm aware that it is not a commonly used language in this field) or with publicly available papers/tutorials/sites/etc. detailing the algorithm so that I could implement it myself.
Thanks in advance for any help!
Look into OpenCV, It has a lot of options for supervised/semi supervised Learning methods. As you have mentioned there is a visible texture difference between the tress and background vegetation, a good place for you would be to start would be color based segmentation and evolving it to use textures as well. OpenCV ML tutorial is a good starting point. Moreover you can also combine the NDVI data to create a stronger feature set.
I was wondering if there is Aforge.NET algorithm that is intended for human activity recognition?
For example, I would like to recognizing drowning while capturing frames from surveillance camera on the beach.
I saw there are algorithms for motion detection, but what I need is motion detection plus logic to process that motion so that computer can conclude does that motion fit into drowning category or any other category I tell him.
Comments would be appreciated.
you might need to develop your own algorythms, i do that too with Aforge
Aforge basicly allows me for simple video aquisition, while my math does the interesting stuff.
In your case..
Detect spots with people
Zoom in to them ??
then it becommes tricky how to distinguish someone who dives from someone who sinks ?..
Also there are waves who can get in front of the person your trying to follow..
usually this recognition comes down to simple observations, like someone pulling his hands up is not like a circle of a swimming head..
you got to think how a beach guard can see the difference, what are the main visual clues and how can you convert them to bitmap math.
Consider using Accord.NET - it's a library based on AForge.NET that contains many machine learning algorithms. However you must write all the logic, as you call it, by yourself.
Another possibility is to use Emgu CV which has some motion detection algorithms.
My requirements:
A user should be able to draw something by hand. Then after he takes off his pen (or finger) an algorithm smooths and transforms it into some basic shapes.
To get started I want to transform a drawing into a rectangle which resembles the original as much as possible. (Naturally this won't work if the user intentionally draws something else.) Right now I'm calculating an average x and y position, and I'm distinguishing between horizontal and vertical lines. But it's not yet a rectangle but some kind of orthogonal lines.
I wondered if there is some well-known algorithm for that, because I saw it a few times at some touchscreen applications. Do you have some reading tip?
Update: Maybe a pattern recognition algorithm would help me. There are some phones which request the user to draw a pattern to unlock it's keys.
P.S.: I think this question is not related to a particular programming language, but if you're interested, I will build a web application with RaphaelGWT.
The Douglas-Peucker algorithm is used in geography (to simplify a GPS track for instance) I guess it could be used here as well.
Based on your description I guess you're looking for a vectorization algorithm. Here are some pointers that might help you:
https://en.wikipedia.org/wiki/Image_tracing
http://outliner.codeplex.com/ - open source vectorizer of the edges in the raster pictures.
http://code.google.com/p/shapelogic/wiki/vectorization - describes different vectorization algorithm implementations
http://cardhouse.com/computer/vector.htm
There are a lot of resources on vectorization algorithms, I'm sure you'll be able to find something that fits your needs. I don't know how complex these algorithms are to implement them, though,
In the upcoming version of Photoshop there is a feature called Content-Aware fill.
This feature will fill a selection of an image based on the surrounding image - to the point it can generate bushes and clouds while being seamless with the surrounding image.
See http://www.youtube.com/watch?v=NH0aEp1oDOI for a preview of the Photoshop feature I'm talking about.
My question is:
How does this feature work algorithmically?
I am a co-author of the PatchMatch paper previously mentioned here, and I led the development of the original Content-Aware Fill feature in Photoshop, along with Ivan Cavero Belaunde and Eli Shechtman in the Creative Technologies Lab, and Jeff Chien on the Photoshop team.
Photoshop's Content-Aware Fill uses a highly optimized, multithreaded variation of the algorithm described in the PatchMatch paper, and an older method called "SpaceTime Video Completion." Both papers are cited on the following technology page for this feature:
http://www.adobe.com/technology/projects/content-aware-fill.html
You can find out more about us on the Adobe Research web pages.
I'm guessing that for the smaller holes they are grabbing similarly textured patches surrounding the area to fill it in. This is described in a paper entitled "PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing" by Connelly Barnes and others in SIGGRAPH 2009. For larger holes they can exploit a large database of pictures with similar global statistics or texture, as describe in "Scene Completion Using Millions of Photographs". If they somehow could fused the two together I think it should work like in the video.
There is very similar algorithm for GIMP for a quite long time. It is called resynthesizer and probably you should be able to find a source for it (maybe at the project site)
EDIT
There is also source available at the ubuntu repository
And here you can see processing the same images with GIMP: http://www.youtube.com/watch?v=0AoobQQBeVc&feature=related
Well, they are not going to tell for the obvious reasons. The general name for the technique is "inpainting", you can look this up.
Specifically, if you look at what Criminisi did while in Microsoft http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.9407 and what Todor Georgiev does now at Adobe http://www.tgeorgiev.net/Inpainting.html, you'll be able to make a very good guess. A 90% guess, I'd say, which should be good enough.
I work on a similar problem. From what i read they use "PatchMatch" or "non-parametric patch sampling" in general.
PatchMatch: A Randomized Correspondence Algorithm
for Structural Image Editing
As a guess (and that's all that it would be) I'd expect that it does some frequency analysis (some like a Fourier transform) of the image. By looking only at the image at the edge of the selection and ignoring the middle, it could then extrapolate back into the middle. If the designers choose the correct color plains and what not, they should be able to generate a texture that seamlessly blends into the image at the edges.
edit: looking at the last example in the video; if you look at the top of the original image on either edge you see that the selection line runs right down a "gap" in the clouds and that right in the middle there is a "bump". These are the kind of artifacts I'd expect to see if my guess is correct. (OTOH, I'd also expect to see them is it was using some kind of sudo-mirroring across the selection boundary.)
The general approach is either content-aware fill or seam-carving. Ariel Shamir's group is responsible for the seminal work here, which was presented in SIGGRAPH 2007. See:
http://www.faculty.idc.ac.il/arik/site/subject-seam-carve.asp
Edit: Please see answer from the co-author of Content-Aware fill. I will be deleting this soon.