On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The NURBS book (2nd ed.)
Graph-Based Methods for Vision: A Yorkist Manifesto
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Shapes and implementations in three-dimensional geometry
Shapes and implementations in three-dimensional geometry
IEEE Transactions on Signal Processing
Regions adjacency graph applied to color image segmentation
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this paper, we propose a hierarchical architecture for representing scenes, covering 2D and 3D aspects of visual scenes as well as the semantic relations between the different aspects. We argue that labeled graphs are a suitable representational framework for this representation and demonstrate its potential by two applications. As a first application, we localize lane structures by the semantic descriptors and their relations in a Bayesian framework. As the second application, which is in the context of vision based grasping, we show how the semantic relations can be associated to actions that allow for grasping without using any object knowledge.