Optimizing computer vision and new tools for perception research

During his internship at Willow Garage, Eric Christiansen from UCSD worked on optimizing computer vision by developing efficient and accurate algorithms for describing and matching local regions of images.

Local image descriptors enable robots to comprehend what they see by describing an image as a set of small and relatively simple parts. These local descriptors can then be matched against datasets of labeled objects which enables new objects to be identified. They can also be re-identified across views of the same scene, to track motion or infer 3D geometry.

By restricting descriptor creation and matching to integer math, Eric and his collaborators created a descriptor which runs efficiently on low-power devices, such as mobile phones and small robots. Also, by creating a technique for very accurate scale and rotation estimation, Eric and his collaborators created another descriptor with an extremely high matching accuracy.

These advances in speed and accuracy should enable robots to see faster and better than they were previously able.

In addition, Eric developed two open source projects during his time at Willow. The first, an automatically-generated Java wrapper for OpenCV, has been previously mentioned, and should make it easier for computer vision researchers to reuse code. The second, Billy Pilgrim (named for a Kurt Vonnegut character) is a framework for evaluating local descriptors. Unlike previous frameworks, this framework integrates with the popular OpenCV library and runs seamlessly on a desktop or cluster. Tools like these will hopefully drive innovation by providing a common platform upon which to develop and test new ideas.