Abstract— This paper discusses the generation of informationrich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of ...
Haralampos-G. D. Stratigopoulos, Salvador Mir, Yio...
Abstract. We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts ...
Given a sample of n observations y1, . . . , yn at time points t1, . . . , tn we consider the problem of specifying a function ˜f such that ˜f • is smooth, • fits the data ...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Particle Filter methods are one of the dominant tracking paradigms due to its ability to handle non-gaussian processes, multimodality and temporal consistency. Traditionally, the e...