bjoernz
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Pedro F. Felzenszwalb and Daniel P. Huttenlocher have published their implementation for the distance transform. You cannot use it for volumetric images, but maybe you can extend it to support 3d data....

An alternative to the Hough Transform would be the Radon Transform (1, 2). A rough description of an algorithm to detect a grid-like structure could look like this: 1. Perform Radon Transform from 0 ...

You could compute the covariance matrix from your elevation data. The eigenvector that belongs to the larger eigenvalue will give you the main direction of variation: http://en.wikipedia.org/wiki/...

To fix the connectivity issue, you can try a close operation: cv::Mat structuringElement = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(40, 40)); cv::morphologyEx( inputImage, outputImage, ...

Just an idea with no guarantee of success: isolate the red blobs (e.g. mark them as white, the rest of the image as black) perform a distance transform for the white blobs (every pixel indicates the ...

I am not sure if Jsteg, Outguess or F5 make already use of this, but in theory you could use the APP segment to hide information. Also the Quantization Table (DQT) could offer some limited space. You ...

As a start, you could look at the distance transform. The distance transform will assign each voxel the minimal distance to the volume boundary. Afterwards you could do a non-maximum suppression. The ...

You want to design a linear transformation, that puts the MIN(I) at 0 and the MAX(I) at 1. A linear equation has the form: y = m*x+b, where m is the slope and b is the point on the y-axis for x = 0. ...

Collections of data sets for various computer vision tasks: http://www.computervisiononline.com/datasets http://www.cvpapers.com/datasets.html

To approximate the volume ($V_{counted}$) you need to count all the voxels. To roughly approximate the surface area ($A_{approx}$) count all the voxels that have an "empty" voxel as a neighbor. (Have ...

It looks like you are looking for algorithms for Content-Based Image Retrieval. As long as you don't expect any miracles, you should be quite happy with a colour histogram approach. Have a look at ...