Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
This paper explores a view-based approach to recognize free-form objects in range images. We are using a set of local features that are easy to calculate and robust to partial occ...
In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probabil...
Joni-Kristian Kamarainen, Miroslav Hamouz, Josef K...
One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modi cation of the curren...
Multiple observation improves the performance of 3D object classification. However, since the distribution of feature vectors obtained from multiple view points have strong nonlin...