A Model-Based 3-D Object Recognition System using Geometric
Hashing with Attributed Features
09:30 a.m., Friday, October 6, 1995
Warren Weaver Hall Room 102
We build an object recognition system that is able to recognize 3-D objects such as vehicles embedded in highly complicated backgrounds. We use the geometric hashing method, augmenting the approach through the use of attributed features, k-d trees for access to features, and the use of bounds in order to limit the search.
We make use of expressive features to improve the performance of a geometric hashing object recognition system. Various kinds of attributed features, such as the midpoint of a line segment with its orientation, the endpoints of a line segment with its orientation, and the center and the circle features are extracted and used in our system.
The number of features as well as the type of features in each model can vary. We make use of weighted voting, which has a Bayesian interpretation. The distribution of the invariants for various features as well as the bounds of the weighted voting formula are analyzed. In order to improve the performance of the system, we use a k-d tree to search entries in high-dimensional hash tables. The method is generalized in order to treat variables taking on values from a non-interval domain, such as data measuring angles. To make use of available computer resources, we distribute the computation, assigning evidence accumulation for a single hypothesis to one processor in a multiple processor and multiple workstation environment. The implementation reduces the communication overhead to minimum. The system is implemented using the Khoros software development system.
The results of target recognition are reported in numerous experiments. The experiments show that the use of more expressive features improves the performance of the recognition system.