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
Related
I am trying to separate the different kinds of grains in an image. And sometimes the image also contains some impurity substance which need to be considered as an extra type.
here are some example images:
corn and beans
long rice and wheat
I tried to find a general method for the different pics, but the result is not good enough.
I used flood-fill and some gradient method to get the regions, and try to use clustering method to classify the contains, but the feature selection is a hard problem, I try gabor filter, but it cannot get me a clear boundary, and so does the classification method such as kmeans.
Any ideas about segmentation, getting the contours or classification will be appreciated. thanks!
I try to post some more pics of my current results, but I am sorry that there is the 2 pics restriction for the beginner here.
It's almost a craft work dealing with image processing problems. I would suggest you to use a robust library (such as OpenCV of course) and use cvFindContours function to identify the contours. Also, search for mathematical morphology. Basic operators such as erosion and dilation may help you since areas of foreground pixels shrink in size, and holes within those areas become larger and vice-versa. Working with color segmentation is also helpful but you might have some troubles since grain color is not uniform. Lastly, feature extraction is another way out. Scale-invariant feature transform can be used to identify every single grain on the image, based on the fact that it is invariant to linear transformations and illumination issues. Hope it helps.
Let's say I have this image this:
With a black scratch and I want to remove it from my image. I know it is noise. I have tried neighbourhood filter and also gaussian filter but no success.
If you know the location of the scratch, this problem is known as inpainting, and there are very sophisticated algorithms for that. So one approach would be to detect the scratch as good as you can, then use a standard inpainting algorithm on it. I've played with your image in Mathematica a little:
First I applied a median filter to the image. As you found out yourself, this removes the scratch, but also removes a lot of detail. The difference between median and original image is a good indicator for your scratch, though:
When I binarize this image with a manually selected threshold, I get a quick&dirty scratch detector:
If you have more knowledge about what your scratches look like, you can improve this detector a lot. e.g. are the scratches always dark? Do they always have high contrast? Are they always smooth curves, i.e. is their curvature always low? - Each of these properties can be measured somehow, so you'd combine these measurements to a single image and binarize that.
One small improvement is to remove small components:
This is still not perfect, but the result is good enough to use it as an inpainting mask:
This will remove some detail, too, but the differences are harder to spot.
Full Mathematica code:
difference = ImageDifference[sourceImage, MedianFilter[sourceImage, 2]];
mask = DeleteSmallComponents[Binarize[difference, 0.15], 15];
Inpaint[sourceImage, mask]
EDIT:
If you're don't have access to a standard inpainting algorithm (like Navier Stokes or Telea), a poor man's algorithm would be to use the median filtered image in those regions where the mask is 1 (probably something like mask*sourceImage + (1-mask)*medialFilteredImage in Matlab). Depending on the image data, the difference might not be worth the extra effort of a "real" inpainting algorithm:
A filter for Avisynth and a plugin for VirtualDub (my two favourite video editing tools). It will hardly get better than these two (You can learn from them if you really need to implement it yourself).
My result using median filter with ImageJ
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,
I want to make an effective illumination compensation on iris images and I want this compensation to be based on color i.e. illumination compensation using color rather than texture. I corrected my images for various mechanical errors but I want a simple algorithm to compensate the illumination based on color. Any ideas?
Try subtracting a low-pass copy of the same image?
What you are interested in is white balancing (i.e. achieving color constancy). One of the simplest algorithms is the Gray-World algorithm and I would try that one first because it's very easy to implement (even though it's not very precise).
You also might want to try some Retinex based algorithms. If so, visit this site: http://www.fer.unizg.hr/ipg/resources/color_constancy/
It contains C++ implementations of several Retinex-based color constancy algorithms.
Do you guys know of any algorithms that can be used to compute difference between images?
Take this webpage for example http://tineye.com/ You give it a link or upload an image and it finds similiar images. I doubt that it compares the image in question against all of them (or maybe it does).
By compute I mean like what the Levenshtein_distance or the Hamming distance is for strings.
By no means do I need to the correct answer for a project or anything, I just found the website and got very curious. I know digg pays for a similiar service for their website.
The very simplest measures are going to be RMS-error based approaches, for example:
Root Mean Square Deviation
Peak Signal to Noise Ratio
These probably gel with your notions of distance measures, but their results are really only meaningful if you've got two images that are very close already, like if you're looking at how well a particular compression scheme preserved the original image. Also, the same result from either comparison can mean a lot of different things, depending on what kind of artifacts there are (take a look at the paper I cite below for some example photos of RMS/PSNR can be misleading).
Beyond these, there's a whole field of research devoted to image similarity. I'm no expert, but here are a few pointers:
A lot of work has gone into approaches using dimensionality reduction (PCA, SVD, eigenvalue analysis, etc) to pick out the principal components of the image and compare them across different images.
Other approaches (particularly medical imaging) use segmentation techniques to pick out important parts of images, then they compare the images based on what's found
Still others have tried to devise similarity measures that get around some of the flaws of RMS error and PSNR. There was a pretty cool paper on the spatial domain structural similarity (SSIM) measure, which tries to mimic peoples' perceptions of image error instead of direct, mathematical notions of error. The same guys did an improved translation/rotation-invariant version using wavelet analysis in this paper on WSSIM.
It looks like TinEye uses feature vectors with values for lots of attributes to do their comparison. If you hunt around on their site, you eventually get to the Ideé Labs page, and their FAQ has some (but not too many) specifics on the algorithm:
Q: How does visual search work?
A: Idée’s visual search technology uses sophisticated algorithms to analyze hundreds of image attributes such as colour, shape, texture, luminosity, complexity, objects, and regions.These attributes form a compact digital signature that describes the appearance of each image, and these signatures are calculated by and indexed by our software. When performing a visual search, these signatures are quickly compared by our search engine to return visually similar results.
This is by no means exhaustive (it's just a handful of techniques I've encountered in the course of my own research), but if you google for technical papers or look through proceedings of recent conferences on image processing, you're bound to find more methods for this stuff. It's not a solved problem, but hopefully these pointers will give you an idea of what's involved.
One technique is to use color histograms. You can use machine learning algorithms to find similar images based on the repesentation you use. For example, the commonly used k-means algorithm. I have seen other solutions trying to analyze the vertical and horizontal lines in the image after using edge detection. Texture analysis is also used.
A recent paper clustered images from picasa web. You can also try the clustering algorithm that I am working on.
Consider using lossy wavelet compression and comparing the highest relevance elements of the images.
What TinEye does is a sort of hashing over the image or parts of it (see their FAQ). It's probably not a real hash function since they want similar "hashes" for similar (or nearly identical) images. But all they need to do is comparing that hash and probably substrings of it, to know whether the images are similar/identical or whether one is contained in another.
Heres an image similarity page, but its for polygons. You could convert your image into a finite number of polygons based on color and shape, and run these algorithm on each of them.
here is some code i wrote, 4 years ago in java yikes that does image comparisons using histograms. dont look at any part of it other than buildHistograms()
https://jpicsort.dev.java.net/source/browse/jpicsort/ImageComparator.java?rev=1.7&view=markup
maybe its helpful, atleast if you are using java
Correlation techniques will make a match jump out. If they're JPEGs you could compare the dominant coefficients for each 8x8 block and get a decent match. This isn't exactly correlation but it's based on a cosine transfore, so it's a first cousin.