Object and scene understanding involves figuring out, at the very least, what is in an image and where things are. Moreover we want to know information about the scene and how objects in it are spatially related. The dominant paradigms treat this as primarily a pattern recognition problem that involves learning some filter-based representation of images that makes the detection and classification problem easier. In contrast, our work on recognition often brings in 3D knowledge about objects in a variety of ways.
Our work addresses:
- object modeling
- object detection
- object recognition
- scene understanding
- scene segmentation
- humans interacting with objects
- machine learning methods
- statistical modeling of scene properties
- geometric models and reasoning