I need to start building a image application and my customer wants to arrange the picutes in the screen like google tv does, as well as everpix. I have been looking for it for a while but I was unable to find it. The result of arranging the pictures this way is amazing and makes the best use of the screen space.
http://www.google.com//tv/static/images/photos_tv_straight.png
Is this a known algorithm? Does it have a name?
Many thanks
T
Like jwpat7 suggested look for "photo collage layout" algorithms. Particularly things like "treemap" and similar (squarified trieemap). I am working on similar algorithm and for some small number of images you just need to solve simple system of linear equations. There is another HP article that is probably more close to what are you looking for.
Mixed-Initiative Photo Collage Authoring - look at part 4.
Following image is done with some squarified treemap and ratio optimization.
Search for photo montage and photo collage algorithms, as well as photo tiling.
An HP article called "Structured Layout for Resizable Background Art" may be helpful.
Numerous collage programs are available for purchase and some software is available in source form; e.g. see hlrnet list, software.informer list, and perhaps this resizing blurb.
The algebra for scaling photos for a collage while maintaining aspect ratios is straightforward and easily described for specific cases, but not for too-general ones.
In css you can arrange images from horizontal to vertical. A good example is the Google image search. There is the Jquery Masonry plugin to arrange from vertical to horizontal and it has some nice animation. In your example you want to have rather a rectangle arrangement I suggest a treemap algorithm where you can also rotate the rectangle in 90°.
Related
Usually the logo detection means find the logo and recognize the logo. Some common works do the two steps together using SIFT/SURF matching method, detailed in
(1) Logo recognition in images
(2) Logo detection using OpenCV
But, if the logo is tiny and blur, the result is poor, and kind of time consuming; I want to split the two steps, firstly finding where the logo is in video; then recognize the logo using template matching or other method, like:
(3) Logo recognition - how to improve performance
(4) OpenCV logo recognition
My problem is mainly focused on finding the logo automatically in video. I tried two methods:
Brightness method. The logo on tv screen usually always there when the show goes on, I select a list of frames randomly and do difference between frames, the logo area tend to be 0; I do some statistics of 0 brightness with threshold to determine whether the pix is logo or not. This method usually do well but failed while the show has static background.
Edge method. Likely, if the logo is there, the border tends to be obvious. I do the statistic work like Brightness method, but edge sometimes unstable，such as very bright background.
Are there any suggestions or state of art methods to auto finding logo areas and any other logo recognition method except sift or template matching ?
Let's assume your list of logos known before hand and you have access to examples (video streams/frames) of all logos.
The 2017 answer to your question is to train a logo classifier, and most likely a deep neural network.
With sufficient training data, if it is identifiable to the TV viewers it will be able to detect it. It will be able to handle local blurring and intensity changes (which may thwart "classic" image processing methods of brightness and edges).
OpenCV can load and run network models from multiple frameworks like Caffe, Torch and TensorFlow, so you can use one of their pre-trainined models or train one yourself.
You could also try the Tensorflow's object detection API here: https://github.com/tensorflow/models/tree/master/research/object_detection
The good thing about this API is that it contains State-of-the-art models in Object Detection & Classification. These models that tensorflow provide are free to train and some of them promise quite astonishing results. I have already trained a model for the company I am working on, that does quite amazing job in LOGO detection from Images & Video Streams. You can check more about my work here: https://github.com/kochlisGit/LogoLens
The problem with the TV is that the LOGOs will probably be not-static and move along the frames. This will result in a motion blur effect, which will probably make your classifier to get confused or not see the LOGOs. However, once you find a logo You can use an object tracking algorithm to keep track of the logo (e.g. deepsort)
live2d can animate a picture and make small movements, just as they show in this video.
1 Does anyone know how it works?
2 Is there any paper describe the mechanism behind it? I tried google scholar search but find few.
3 Is there any open source work on this field?
The fundamental algorithm is like control points in Adobe Illustrator. Control points are like anchors for the image. You can shrink, stretch and bend the image by moving the control points.
Unfortunately, NO. Live2D is all developed in the company.
For now it is all closed project.
I am trying to build a script that will dynamically arrange photos like a collage very similar to what is done on http://lightbox.com/explore#spotlight.
I can off course write code that would handle each case with different sets of photos but I would prefer to have an algorithm that would be able to handle any number of photos.
The algorithm explained here http://www.hpl.hp.com/techreports/2008/HPL-2008-199.pdf in chapter 4 seems very similar to what I need to do.
In my case the vertical and horizontal ratios would always be the same. I would have a defined a bounding box and how many levels each node could get split. The bounding box would have the same ratio of a horizontal photo. If the algorithm can't fit all images I would go back one level and leave it there or pick another photo from a pool of available photos.
My question is very similar to this one Algorithm Arrange images on screen but I am not sure how to move forward. Any further guidance or pseudo code would be very helpful.
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.
Recently I've been messing about with algorithms on images, partly for fun and partly to keep my programming skills sharp.
I've just implemented a 'nearest-neighbour' algorithm that picks n random pixels in an image, and then converts the colour of each other pixel in the image to the colour of its nearest neighbour in the set of n chosen pixels. The result is a kind of "frosted glass" effect on the image, for a reasonably large value of n (if n is too small then the image gets blocky).
I'm just wondering if anyone has any other good/fun algorithms on images that might be interesting to implement?
Tom
This book, Digital Image Processing, is one of the most commonly used books in image processing classes, and it will teach you a lot of basic techniques that will help you understand other algorithms better, like the ones Ants Aasma suggested.
Try making an Andy Warhol print. It's pretty easy in Java. For more ideas, just look at the filters available in GIMP or a similar program.
Marching Squares is a computer vision algorithm. Try using that to convert black and white raster images to object based scenes.
Turns the image into a pizza
Take N images, relate them via an MC-Escher-style painting
"Explode" an image from the inside out
Convert the image into a single-color blocks (piet-style) based on all the colours within.
How about tie-dye algorithm?
Fun to toy with and easy to code filters are:
kaleidoscope
lens
twirl
There are a lot of other filters, but especially the kaleidoscope gives much bang for the bucks. I have made my own graphics editor with lots of filters and is also looking for inspiration.
Instead of coding image filters, I personally would love to code Diffusion Curves, but unfortunately have little time for fun.
If you want to try something more challenging look for SIGGRAPH papers on the web. There are some really nifty image algorithms presented at that conference. Seam carving is one cool example that is reasonably straightforward to implement.
If you want something more challenging try to complete the symmetry of broken objects