We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D lo...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
It is a common human behavior to hold a small object of interest and to manipulate it for observation. A computer system, symbiotic with a human, should recognize the object and th...
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...
The visual detection and recognition of objects is facilitated by context. This paper studies two types of learning methods for realizing context-based object detection in paintin...
Niek Bergboer, Eric O. Postma, H. Jaap van den Her...
We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...