This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-Means algorithm can be described either as a gradient descent algorithmor by sl...
In this paper we propose a framework for gradient descent
image alignment in the Fourier domain. Specifically,
we propose an extension to the classical Lucas & Kanade
(LK) a...
: Nonnegative matrix approximation (NNMA) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields with applications ranging from...
We present a fast, robust and automatic method for computing central paths through tubular structures for application to virtual endoscopy. The key idea is to utilize a medial sur...
Compressed sensing or compressive sampling (CS) has been receiving a lot of interest as a promising method for signal recovery and sampling. CS problems can be cast as convex prob...
Seung-Jean Kim, Kwangmoo Koh, Michael Lustig, Step...