There is a very large image . Now this image is split into small size images(where if we put all these small size images at their respective positions it forms the original image).
I want to map these small images to a face of a cube at their respective positions. So that it should look as the mapping the original image itself.
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I am building a viewer in threejs, and mapping the original image file onto the sphere works fine, but it takes a large amount of time to get the file from my image server. I have the images split into smaller files as tiles, and I was wondering if there is a way to map these individual files onto a sphere to reduce the load time.
Suppose we have a generic red/cyan 3D anaglyph photo. How can it be processed so a depth map is extracted?
An anaglyph is just a superposition of a left-eye and right-eye image, using different colours for each.
Assuming you can use the colour components to extract the original greyscale left and right images, the problem is no different to any stereo vision problem. You need to determine the epipolar geometry, perform rectification on one of the images, then create a disparity map to derive relative depth information.
I have bunch of images of cells from different channels, imageA_c1.tif, imageA_c2.tif and imageA_c3.tif
I wish to clip a bounding box through all the images at the exact same location.
So I imported the 3 images.
Then I align them and group them.
I then set a rectangle at a region of interest on top of these grouped images and then do Path->clip
Clipping is fine.
However, when I ungroup the objects, the clipping is lost and the images revert to their full original size.
Is there a way to retain the crop images using a bounding box through a stack of images and retain them after ungrouping?
Thanks
Lee
I am attempting to create a content based image retrieval system (CBIR) in MATLAB for colour images, and am using a k-means algorithm to extract the feature vectors for images in my database. Each image has four clusters, and each cluster has information about the colour (R,G,B) and position (X,Y).
I am now trying to add a texture feature to my clusters, and need to use grey level co-occurrence matrices (GLCM) for this. I know that GLCM is just an indicator of probability that a certain grey level will appear next to another, and have created the GLCM for my images.
I am unclear about how to map the GLCM to the original image (and thus its clusters), since GLCM talks about pairs of pixels, and I would like each X,Y position to have texture information. How does one go about translating GLCM to pixels?
The output of GLCM seems to be a T-by-T matrix where T is the number of distinct grayscale levels in the image. Therefore, the size of this matrix does not really depend on the size of your image. The matrix also describes the texture of the whole image, so it isn't especially meaningful to associate GLCM data with a single pixel.
It sounds like you could compute GLCM for the individual clusters, since this would describe the texture within that cluster? I think graycomatrix requires a rectangular image, but you could find the bounding box for each cluster and extract GLCM from them separately.
If you wanted to get some more meaningful information out of a GLCM matrix (i.e. something that is appropriate as a 'feature'), you could use graycoprops which returns 4 summary statistics.
Does opengl provide any facilities to help with image smoothing?
My project converts scientific data to textures, each of which is a single line of colored pixels which is then mapped onto the appropriate area of the image. Lines are mapped next to each other.
I'd like to do simple image smoothing of this, but am wondering of OGL can do any of it for me.
By smoothing, I mean applying a two-dimensional averaging filter to the image - effectively increasing the number of pixels but filling them with averages of nearby actual colors - basically normal image smoothing.
You can do it through a custom shader if you want. Essentially you just bind your input texture, draw it as a fullscreen quad, and in the shader just take multiple samples around each fragment, average them together, and write it out to a new texture. The new texture can be an arbitrary higher resolution than the input texture if you desire that as well.