I have a bunch of low contrast images of white LEGO parts and pieces as shown. Since it has very low contrast and most of the edge detection algorithm does not perform well. The particular edge I need is the outermost circumference.
I tried applying some filters, but these images clearly need some pre-processing. Could you please suggest what I could do to get better results. I tried searching online but the solutions I found either don't work or sound like magic to me.
LEGO PARTS IMAGE
THE EDGE I NEED
Doing contrast stretching could help in low contrast images. see the opencv documentation for more information from here.
Related
I'm looking for a program that will enable me to quantity the difference between images in an image sequence over time.
We are hoping to use timelapse images to measure the activity of tadpoles by comparing how the images change over time. Tracking the movement of individuals isn’t necessary. The tadpoles are dark and the background of the aquarium is light, however the background isn’t uniform and some of the decor items like dark rocks and foliage make it so that all the tadpoles aren’t visible at all times.
Basically need a program that will allow me to quantity the differences/motion detected in an image sequence (i.e 209 images) and produce data that can be exported...
Any and all suggestions appreciated!!
Your question is rather vague and you don't supply any images or real indication of what you expect as results, so my answer will not be as thorough as it might otherwise be.
You don't mention any tools you are familiar with, but my recommendation would be Python and OpenCV. Alternatives are probably scikit-image, Python Wand.
In general, when trying to detect movement across a series of images, you would:
try and work out what the background is
look for movement by sutracting, or differencing, frames from the background
clean up the difference image
identify objects - maybe by shape or size or colour
maybe track objects
produce statistics
As regards working out the background, I did an example here by finding the median pixel across all images at each location in the images. There is also an OpenCV tutorial here.
As regards cleaning up images, you can probably remove noise in the background subtraction with a small median filter, say 3x3 or 5x5 depending on the resolution of your images.
As regards detecting tadpoles, you will probably want to use OpenCV findContours() and filter by size, or colour, or circularity. There are some fairly decent tutorials on PyImageSearch. There is also an ImageMagick "Connected Component" analysis to find a tennis player that I did here.
I was wondering if its possible to match the exposure across a set of images.
For example, lets say you have 5 images that were taken at different angles. Images 1-3,5 are taken with the same exposure whilst the 4th image have a slightly darker exposure. When I then try to combine these into a cylindrical panorama using (seamFinder with: gc_color, surf detection, MULTI_BAND blending,Wave correction, etc.) the result turns out with a big shadow in the middle due to the darkness from image 4.
I've also tried using exposureCompensator without luck.
Since I'm taking the pictures in iOS, I maybe could increase exposure manually when needed? But this doesn't seem optimal..
Have anyone else dealt with this problem?
This method is probably overkill (and not just a little) but the current state-of-the-art method for ensuring color consistency between different images is presented in this article from HaCohen et al.
Their algorithm can correct a wide range of errors in image sets. I have implemented and tested it on datasets with large errors and it performs very well.
But, once again, I suppose this is way overkill for panorama stitching.
Sunreef has provided a very good paper, but it does seem overkill because of the complexity of a possible implementation.
What you want to do is to equalize the exposure not on the entire images, but on the overlapping zones. If the histograms of the overlapped zones match, it is a good indicator that the images have similar brightness and exposure conditions. Since you are doing more than 1 stitch, you may require a global equalization in order to make all the images look similar, and then only equalize them using either a weighted equalization on the overlapped region or a quadratic optimiser (which is again overkill if you are not a professional photographer). OpenCV has a simple implmentation of a simple equalization compensation algorithm.
The detail::ExposureCompensator class of OpenCV (sample implementation of such a stitiching is here) would be ideal for you to use.
Just create a compensator (try the 2 different types of compensation: GAIN and GAIN_BLOCKS)
Feed the images into the compensator, based on where their top-left cornes lie (in the stitched image) along with a mask (which can be either completely white or white only in the overlapped region).
Apply compensation on each individual image and iteratively check the results.
I don't know any way to do this in iOS, just OpenCV.
I am working on images that are partially blur on some sections. These are noises that should be taken care of, but here is the problem:
Are there methods to detect whether an image is blur or partially blur at some sections of an image? For instance, take a look at sample image below:
You can see in the image that there are 3 sections that are visually blur: bottom-left, near center region and top-right. Now, is it possible to detect that any portion of an image is blur programming-wise or mathematically?
As lain_b pointed out, with an image like this you can use an edge detector and look for an absence of edges. I tried it on your image and it seems to work pretty well. First I used the kernel
[0,1,0,
1,-4,1,
0,1,0]
Which is a simple edge detector. Its result was
Then I used a threshold to get
Then I closed the image and opened it to get
This is obviously not a finished version, the top right portion did not recognize well at all. Perhaps you could improve it by blurring before performing thresholding, or by choosing better values for the threshold and the radii of the opening and closing operations. A lot of the decisions you will need to make depend on the constraints you can put on your problem. I think this technique will work for you though.
Edit
If you are looking for blur detection of arbitrary images you are going to have to investigate a wide variety of techniques. Things are much easier if you can make assumptions about your set of input images. Without any assumptions I don't know what will work best for you. Here is some reading on the topic
Image Blur Metrics
Reserach paper on using the Harr wavelet transform
Similar SO Question and look at the question that question links to
Blur detection is a very active research field, there is no one answer. You will just need to try all the methods you can find (these were found by googling detect blur in image).
This paper may be of some help. It does blur estimation (mostly for out of focus, but I think it also does blur) to recreate a similarly blurred object in the image.
I think you should be able to use it to detect the blurred areas, and how blurred they are. It should be especially relevent to your problem as it is designed to work with real-world images.
The objective is to display the person on a different background (aka background removal).
I'm using the Kinect with Microsoft's Beta Kinect SDK to do so. With help of the depth, the background is filtered and we get only the image of the person.
This is pretty simple to do, and we can find the code that does that everywhere on the Internet. However, the depth signal is noisy, and we get pixels which do not belong to the person that are displayed.
I applied an edge detector to see if it was useful, and I currently get this:
Here's another without edge detection:
My question is: Which way can I get rid of these noisy white pixels around the person?
I tried morphological operations, but some parts of the body are erased and still leave white pixels behind.
The algorithm doesn't need to be real-time, I can just apply it when I press a 'Save image' button.
Edit 1:
I just tried to do background substraction with the closest frames on the shape border. The single pixels you see are flickering, which means it is noise and I can get easily get rid of them.
Edit 2:
The project is now over, and here's what we did: manual calibration of the Kinect by using the OpenNI driver, which provides directly the infrared image. The result is really good, but each calibration is specific to each Kinect.
Then, we applied a little transparency on the borders, and the result looks really nice! I can't provide pictures, however.
Your problem isn't just the noisy white pixels. You're missing significant parts of the person as well, e.g. part of his right hand. I'd recommend being more conservative with your thresholding of the depth data (allow more false positives). This would give you more noisy pixels, but at least you'd have the person in their entirety.
To get rid of the noisy pixels, I can think of a couple of things:
Feather the outer pixels (reduce them in intensity/increase their transparency if you're using an alpha channel)
Smooth the image, perform the edge detection on the smoothed image, then use these edges with your original sharp image.
Do some skin region detection to mark parts that definitely belong to a person. See skin detection in the YUV color space? and Skin Color Detection
For clothes, work with the hue and saturation image. If you know the color of the t-shirt (or that at least that it's not a neutral color), then this will stand out easily. If you don't know this information, then it may be worth building up a model of the person using the other frames (if there's a big gray blob that's moving around in your video, chances are that your subject is wearing a gray shirt)
The approaches aren't mutually exclusive so it may be worth trying to do them in combination. If I think of anything else, I'll post back here.
If there is no other way of resolving the jitter on the edges you could always try anti-alias as post-process.
I need to sort a huge number of photos, and remove the blurry images (due to camera shake), the over/under exposed ones and detect whether the image was shot in the landscape or portrait orientation. Can these things be done on an image using an image processing library or are they still beyond the realms of an algorithmic solution ?
Let's look at your question as three separate question.
Can I find blurry images?
There are some methods for finding blurry images either from :
Sharpening an image and comparing it to the original
Using wavelets to detect blurring ( Link1 )
Hough Transform ( Link )
Can I find images that are under or over exposed?
The only way I can think of this is that your overall brightness is either really high or really low. But the problem is that you would have know if the picture was taken at night or day. You could create a histogram of your image and see if it is really skewed one way or the other and that might be some indication of over/under exposure.
Can I determine the orientation of the image?
There are techniques that have been used such as SVM, Color Moments, Edge Direction Histograms, Bayesian Framework using cues.
Can I find images that are under or over exposed?
here histograms is recommended.