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- | ====== Finding Objects in Cluttered Scenes for Home Robotics ====== | ||
- | **[[http://www.cs.ubc.ca/~little/|Jim Little]]**\\ | ||
- | **Computer Science**\\ | ||
- | **University of British Columbia** | ||
- | |||
- | **Tuesday April 24 2012, 14:15-15:30**, Viktor Franz Hess building, HS D | ||
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- | We want computers and robots to observe us and know who we are and | ||
- | what we are doing, and to understand the objects and tasks in our | ||
- | world, both at work and in the home. We have built systems that enable | ||
- | mobile robots to find objects using visual cues and learn about shared | ||
- | workspaces. | ||
- | |||
- | We've demonstrated these abilities on [[http://www.cs.ubc.ca/labs/lci/curious_george/|Curious George]], our | ||
- | visually-guided mobile robot that has competed and won the Semantic | ||
- | Robot Vision Challenge at AAAI (2007), CVPR (2008) and ISVC (2009), in | ||
- | a completely autonomous visual search task. In the SRVC visual | ||
- | classifiers are learned from images gleaned from the Web. Challenges | ||
- | include poor image quality, badly labeled data and confusing semantics | ||
- | (e.g., synonyms). Clustering of training data, image quality analysis, | ||
- | and viewpoint-guided visual attention enable effective object search | ||
- | by a home robot. But there remain many interesting challenges because | ||
- | objects are hidden by others and can only be seen from some | ||
- | viewpoints. We will discuss how to reason about viewpoint and | ||
- | recognition in cluttered scenes, using standard images and range data | ||
- | from the Kinect sensor. | ||