Many problems in vision can be formulated as Bayesian inference. It is important to determine the accuracy of these inferences and how they depend on the problem domain. In recent...
Alan L. Yuille, James M. Coughlan, Song Chun Zhu, ...
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
This paper describes a new method for tracking of a human body in 3D motion by using constraints imposed on the body from the scene. An image-based approach for tracking exclusive...
Multi-resolution techniques have been used in a wide range of vision applications. Unfortunately, the costly operation of building a proper pyramid strongly reduces its value as a...
In Proc. of IEEE Conf. on CVPR'2000, Vol.I, pp.222-227, Hilton Head Island, SC, 2000 In many vision applications, the practice of supervised learning faces several difficulti...
This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature....
Yongmei Wang, Bradley S. Peterson, Lawrence H. Sta...
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
We consider a scene, containing many objects moving with constant velocity along straight line paths, seen from three reference viewpoints at three different times.The scene may e...
The motion of a non-rigid scene over time imposes more constraints on its structure than those derived from images at a single time instant alone. An algorithm is presented for si...
Sundar Vedula, Simon Baker, Steven M. Seitz, Takeo...