We present a method for the simultaneous detection and segmentation of objects from static images. We employ lowlevel contour features that enable us to learn the coarse object sh...
A variety of flexible models have been proposed to detect
objects in challenging real world scenes. Motivated
by some of the most successful techniques, we propose a
hierarchica...
Paul Schnitzspan (TU Darmstadt), Mario Fritz (Univ...
We present a method for the simultaneous detection and segmentation of people from static images. The proposed technique requires no manual segmentation during training, and explo...
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of featur...
Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang, ...
We introduce an approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. This approach provides an effici...