Motion and Perception

Motion and perception in the field of artificial intelligence has a relatively recent history when compared to other fields in computer science. Along with the premature boom and bust of artificial intelligence in decades past, computer vision and related fields in AI have been slowly developing fundamental methods to have computers be able to sense and analyze its surroundings. Motion is strongly linked with the field of robotics, as the application of this type of research will profoundly impact the way in which we interact with computers, and computers begin to interact with the world. This sub-field of artificial intelligence has a few main parts: robotics, machine perception, computer vision, and speech recognition. Robotics, at its core according to futurist and inventor Ray Kurzweil, is the study of navigation, localization (knowing where you are), mapping, or learning what is around you, and motion planning, which is figuring out how to get somewhere.

Relevant Link(s):
Ray Kurzweil

Sunday, May 4, 2008

Interview with Professor David Jacobs

In an interview I performed with Professor David Jacobs of the University of Maryland, he said in the next five to ten years, major advances in the field will allow this field of computer science to break out into the mainstream consumer culture, and impact the lives of everyone. As a researcher of computer vision, he stated that computer vision is the study of trying to get information out of images, to understand the world through images in other words, and to understand motion. The techniques used in this field involve three-fold:
  1. Analyze image using image processing.
  2. Analyzing geometry and optimization thereof.
  3. Machine learning using different types of training data.

At its face, these problems seem fairly abstract, but they are all an effort to overcome the challenges that still lie ahead in the field. Dr. Jacobs asserts that the largest obstacle to wide-spread use of this technology is that is it very difficult to get a relationship between two-dimensional and three-dimensional objects. The most interesting problem he is tackling right now is collaborating with the Smithsonian institute on tacking pictures of leaves and being able to identify species. This is done by attempting to analyze the difference in shapes between leaves, using silhouette processing. All techniques discussed above are being used to figure out an optimal solution.
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