Event Location: 1507 NSH
Bio: Daniel is a Masters student in the Robotics Institute at Carnegie Mellon University (CMU). He graduated from CMU in 2007, receiving a B.S. in Electrical and Computer Engineering with a minor in Computer Science. His research interests include machine learning and computer vision for automated scene interpretation.

Abstract: We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.