We present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. The model generation is structured ...
Igor Vainer, Sarit Kraus, Gal A. Kaminka, Hamutal ...
Abstract. Shape features applied to object recognition has been actively studied since the beginning of the field in 1950s and remain a viable alternative to appearance based metho...
Subspace-based methods rely on dominant element selection from second order statistics. They have been extended to tensor processing, in particular to tensor data filtering. For t...
The estimation of high-dimensional probability density functions (PDFs) is not an easy task for many image processing applications. The linear models assumed by widely used transf...
Parallel scalability allows an application to efficiently utilize an increasing number of processing elements. In this paper we explore a design space for parallel scalability for...