𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Learning Task-Specific Object Recognition and Scene Understanding

✍ Scribed by Tom Drummond; Terry Caelli


Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
688 KB
Volume
80
Category
Article
ISSN
1077-3142

No coin nor oath required. For personal study only.

✦ Synopsis


In this paper, we present an approach to object recognition and scene understanding which integrates low-level image processing and high-level knowledge-based components. A novel machine learning system is presented which is used to acquire knowledge relating to a specific task. Learned feedback from high-level to low-level processes is introduced as a means of achieving robust task-specific segmentation. The system has been implemented and trained on a number of scenarios with differing tasks from which results are presented and discussed.


πŸ“œ SIMILAR VOLUMES


Learning and understanding dynamic scene
✍ Hilary Buxton πŸ“‚ Article πŸ“… 2003 πŸ› Elsevier Science 🌐 English βš– 326 KB

We are entering an era of more intelligent cognitive vision systems. Such systems can analyse activity in dynamic scenes to compute conceptual descriptions from motion trajectories of moving people and the objects they interact with. Here we review progress in the development of flexible, generative

Object recognition and articulated objec
✍ Wen-Jing Li; Tong Lee πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 560 KB

In this paper, a novel object recognition method based on attributed relational graph matching is proposed, which is called accumulative HopΓΏeld matching. We ΓΏrst divide the scene graph into many sub-graphs, and a modiΓΏed HopΓΏeld network is then constructed to obtain the sub-graph isomorphism betwee

Learning visually grounded words and syn
✍ Deb K. Roy πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 478 KB

A spoken language generation system has been developed that learns to describe objects in computer-generated visual scenes. The system is trained by a 'show-and-tellΓ• procedure in which visual scenes are paired with natural language descriptions. Learning algorithms acquire probabilistic structures