Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to...
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of c...
Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain clas...
Maria Klodt, Thomas Schoenemann, Kalin Kolev, Mare...
Many methods for 3D reconstruction in computer vision rely on probability models, for example, Bayesian reasoning. Here we introduce a probability model of surface visibilities in ...
This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We s...
This paper introduces a novel method for recovering both the light directions and camera poses from a single sphere. Traditional methods for estimating light directions using spher...
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the...
In this paper, we present a robust face alignment system that is capable of dealing with exaggerating expressions, large occlusions, and a wide variety of image noises. The robustn...