VASC Seminar: A Data-Driven Vision Compiler for Automatic Object Pose Recognition

VASC Seminar
Monday, May 11, 2009
3:30p-4:30p
NSH 1507

A Data-Driven Vision Compiler for Automatic Object Pose Recognition

Rosen Diankov
Carnegie Mellon University, Robotics Institute


Abstract:

This presentation focuses on an object-specific vision system that detects and extracts the precise 6D pose of objects in an image. The system builds a data-driven statistical model of the expected features of an object's surface and combines this with a discrete search method
to extract the pose of all object. The training phase of the vision system can be interpreted as a compiler that automatically analyzes the statistics of how the features are distributed on the object and determines a feature set's stability and discriminable power. This compilation phase requires the precise CAD model of an object along with a training set of real-world images. After compilation, a CAD-independent model of how features relate with respect to theobject's pose and inter-relate with each other is created. These relationships allow both point-based features like SIFT and edge-based features to be used simultaneously when computing the 6D pose of an
object. Using this data-driven model, we employ a discrete randomized search with RANSAC to find the poses of all instances of the object in a novel image.


Bio:

Rosen Diankov graduated from University of California Berkeley in 2006 with Electrical Engineering and Computer Science, and Applied Math degrees. At the moment he is a PhD graduate student at the Robotics Institute at Carnegie Mellon University. Rosen's main research focus is tackling the robotics problem: combining perception, planning, and control into one coherent framework. Up until now he has worked on several vision and planning systems involving autonomous robots in everyday scenarios both in the United States and Japan.

VASC Seminars are sponsored by Tandent Vision Science, Inc.