This site uses cookies to deliver our services and to ensure you get the best experience. By continuing to use this site, you consent to our use of cookies and acknowledge that you have read and understand our Privacy Policy, Cookie Policy, and Terms
—Sparse representations have emerged as a powerful tool in signal and information processing, culminated by the success of new acquisition and processing techniques such as Compr...
We present a novel approach to integrate transliteration into Hindi-to-Urdu statistical machine translation. We propose two probabilistic models, based on conditional and joint pr...
Nadir Durrani, Hassan Sajjad, Alexander Fraser, He...
Most tracking algorithms implicitly apply a coarse segmentation of each target object using a simple mask such as a rectangle or an ellipse. Although convenient, such coarse segme...
In this paper, the feasibility of using finite totally ordered probability models under Aleliunas’s Theory of Probabilistic Logic [Aleliunas, 1988] is investigated. The general...
We revisit the idea of history-based parsing, and present a history-based parsing framework that strives to be simple, general, and flexible. We also provide a decoder for this pr...
Abstract. We propose a new unsupervised training method for acquiring probability models that accurately segment Chinese character sequences into words. By constructing a core lexi...
Mixed Group Ranks is a parametric method for combining rank based classiers that is eective for many-class problems. Its parametric structure combines qualities of voting methods...
In many different application areas, e.g. space observation systems or engineering systems of world-wide operating companies, there is a need for an efficient distributed intersect...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...
Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized ...