I have several images like the one below which are coming from a scanned book.
The images have been catted with the same image-size but they are slightly distorted and do not overlap perfectly. You can see an animation here https://dl.dropboxusercontent.com/u/29337496/animation.gif .
Before apply a georeferencing process (by gdal) i need to align them in order to have the country borders in perfect overlap.
I have already test align_image_stack (hugin sw) with the different flags,
but i did not get positive results.
Any idea?
I'm using Ubuntu.
Thanks
Best Giuseppe
Related
I want to find if two images are the picture of the same art work (below are two pics of a work from japanese artist Yoshitomo Nara)
I aligned the two pics using feature match and affine transformation. So the images are now of identical size and supposedly well aligned.
but in some cases (e.g. in this one), the two pictures have different brightnesses.
directly comparing greyscale images doesn't work.
I tried equal hist, which makes the situation even worse
I also tried some other methods, including:
number of matches in feature match;
ssim https://en.wikipedia.org/wiki/Structural_similarity
phash https://www.phash.org/
none of them work very well.
My guess is I need to first get rid of the brightness difference. But I'm not sure how to do it. Any ideas? Thanks
I have about 6000 aerial images taken by 3DR drone for vegetation sites.
The images have to overlap to some extant because the drone flights cover the area go from EW and then again NS, so the images present the same area from two directions. I need the overlap for the images for extra accuracy.
I don't know to write a code on IDL to combine the images and create that overlap. Can anyone help please?
Thanks
What you need is something that is identifiable that occurs in both images. Preferably you would have several things across the field of view so that you could get the correct rotation as well as a simple x-y shift.
The basic steps you will need to follow are:
Source Identification - Identify sources in all images that will later be used to align the images. Make sure the centering of these soruces are good so that they will align better later.
Basic alignment. Start with a guess on where the images should align then try to match the sources.
Match the sources. There are several libraries that can do this for stars (in astronomical images) that could be adapted for this.
Shift and rotate the images. This can be done to the pixels or to the header that is read in and have a program manipulate the pixels on the fly.
I'm trying to figure out how i can detect parts of an image based on color and/or shape.
Ideally what i want to achieve is that:
given a lot of pictures which are similar in some way
they always contain several identical but a non-trivial shape (e.g.
variations of finger nails, not just simple squares or circles)
figure out these interesting areas somehow, so i can detect the color of these areas
Example:
Assume that i have a lot of these images of fingers, i would like to detect which color the nails have. This is a nice example of how finger nails are "quite the same shape" and are "quite similar in color".
In the end i should be able to figure out these interesting areas such that i only get the blue nails (== shape) in a picture and nothing else.
What would be the best way to do this?
I thought of the following things which might help me, however i'm unsure on how to do this properly.
edge detection
detect colors in a given image using color quantization
cut out some nail shapes and match them to the picture (but too intensive and too many variations to collect?!)
imagemagick is a tool i can use which supports everything i need for this (i think)
Preferrably i would like to do this using node.js
I'm trying to align several images in matlab and I'm having trouble getting matlab to align them properly. I want to align them so that I can overlay them to make a larger image by stitching/averaging them together. I posted several of the images here. While it is not difficult to align 5 images manually I want to make a script to do it so that I do not need to align hundreds of similar images manually as well.
Over the past few days I've tried several ways of getting this to work. My thought was that if I could filter the images enough then I could make a mask for the letters and then it would be easy to align them--but I haven't been able to make it work.
I've tried using local adaptive thresholding to compensate for a nonuniform brightness level across the picture but it hasn't allowed me to align them properly. For the actual image alignment I've been using imregister() and normxcorr2() but both do not properly align the images.
I don't think that this should be that difficult to do but I haven't been able to do it. Any thoughts or insight would be greatly appreciated.
Edit: uploaded images after performing various operations here
I would do some feature detection, followed by the RANSAC algorithm to find the transformation between any pair of images.
see this demo on the Mathworks web-site: http://www.mathworks.com/videos/feature-detection-extraction-and-matching-with-ransac-73589.html
I realize there might be a better place to ask this, but I think you all will have some valuable feedback.
People are asked to draw a shape in black on a white cavas. Then their drawing is added to the running average. I'd like to have the parts that the images mostly have in common be shown, and the parts of the drawing that are unlike most of the other drawings disappear.
My two problems are that I'm using ImageMagick to process the images, which means that I can only create a composite of two images at once. So I have the running total image, and the newest one to add. I cannot get a real average this way.
Secondly, I do not fully understand blending modes particularly when different opacities are involved. I'm not sure which is the best to use.
When you add the first two images you blend them equally. But then when you add the 3rd image to the mix you have to change the weight of each image. The two image composite should be blended at 66.6% while the new image should contribute just 33.3%. For the 4th image you will blend at 75% and 25% respectively. In general, if you have n images in the composite, then the new image should contribute 100/(n+1) percent when added.
As you see, the more images you have the less an individual image affects the composite result.