8. A computer-readable medium having computer executable instructions stored thereon for automatically learning a set of weak classifiers, comprising instructions for: receiving a set of weighted positive examples corresponding to instances in a set of training data; receiving a set of weighted negative examples not corresponding to instances in the set of training data; specifying a number of weak classifiers to be learned; selecting a unique rectangular filter for each of the weak classifiers to be learned; for detection windows of each rectangular filter, constructing a histogram of the weights of the positive and negative examples; iteratively transforming each histogram into a histogram having a variable number of variably sized bins; and outputting each transformed histogram to form a set of weak classifiers.
12. The computer-readable medium of claim 8 wherein iteratively transforming each histogram into a histogram having a variable number of variably sized bins comprises optimizing each corresponding weak classifier by maximizing a ratio of positive to negative examples for each bin of the transformed histogram.