In k-means clustering we are given a set of n data points in d-dimensional space and an integer k, and the problem is to determine a set of k points in d-space, called centers, so as to minimize the mean squared distance from each data point to its neares
Anat Levin, Dani Lischinski, Yair Weiss - a computer-assisted process of adding color to a monochrome image or movie with a relatively modest amount of user input.
by Bill Green. using the Sobel and the Laplace methods."Edges characterize boundaries and are therefore a problem of fundamental importance in image processing"
By Oleg Krivtsov - Implementing Lucas-Kanade and Baker-Dellaert-Matthews image alignment algorithms (remember to check l8r freaking site changes the urls)
an approach to simulating very large textures using much less texture memory than they'd require in full by downloading only the data that is needed, and using a pixel shader to map from the virtual large texture to the actual physical texture.
Image alignment is the process of matching one image called template (let's denote it as T) with another image, I (see the above figure). There are many applications for image alignment, such as tracking objects on video, motion analysis, and many other tasks of computer vision. In 1981, Bruse D. Lucas and Takeo Kanade proposed a new technique that used image intensity gradient information to search for the best match between a template T and another image I. The proposed algorithm has been widely used in the field of computer vision for the last 20 years, and has had many modifications and extensions. One of such modifications is an algorithm proposed by Simon Baker, Frank Dellaert, and Iain Matthews. Their algorithm is much more computationally effective than the original Lucas-Kanade algorithm.