In this paper, a new approach for object detection and pose estimation is introduced. The contribution consists in the conception of entities permitting stable detection and relia...
Stefan Hinterstoisser, Selim Benhimane, Nassir Nav...
Today's category-level object recognition systems largely focus on fronto-parallel views of objects with characteristic texture patterns. To overcome these limitations, we pr...
Object detection can be challenging when the object class exhibits large variations. One commonly-used strategy is to first partition the space of possible object variations and t...
Quan Yuan, Ashwin Thangali, Vitaly Ablavsky, Stan ...
Previous pixel-level change detection methods either contain a background updating step that is costly for moving cameras (background subtraction) or can not locate object positio...
Object detection is challenging partly due to the limited discriminative power of local feature descriptors. We amend this limitation by incorporating spatial constraints among ne...
Color is a powerful visual cue for many computer vision
applications such as image segmentation and object recognition.
However, most of the existing color models depend on the i...
Jean-Philippe Tarel graduated from the Ecole Nationale des Ponts et Chaussées (ENPC), Paris, France (1991). He received his PhD degree in Applied Mathematics from Paris IX-Dauphin...
We propose a new approach for detecting low textured
planar objects and estimating their 3D pose. Standard
matching and pose estimation techniques often depend on
texture and fe...
Stefan Holzer, Stefan Hinterstoisser, Slobodan Ili...