4. An active learning system as set forth in claim 3 , wherein the feature vector is based on local intensity variations present in an image region around the point as seen from a given view, such that a number of points that form the object model is selected based on local photometric energy at each point, with the point being retained as an actual object point if its photometric energy is greater than a threshold where the threshold is computed based on the photometric energy associated with points within the image region.
3. An active learning system as set forth in claim 2 , further comprising: a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate; a static classifier for initial or temporary classification of the potential object candidate, thereby generating a classification category selected from a group consisting of a positive identification, a negative identification, a false positive identification, and a false negative identification, and where upon classification, the static classifier communicates the classification to the incremental learning module.