Paul A. Viola
We describe a technique, called feature-based recognition (FBR), that correctly classifies images of objects under perturbation by noise, rotation and scaling. FBR uses a set of feature detectors to build a representation vector for images. The feature detectors are learned from the dataset itself. A second system, called FBR+, uses a simple neural network to significantly improve generalization to novel images.