  We propose a new feature extraction method based on two dynamical systems
induced by intensity landscape: the negative gradient system and the
Hamiltonian system. We build features based on the Hamiltonian streamlines.
These features contain nice global topological information about the intensity
landscape, and can be used for object detection. We show that for training
images of same size, our feature space is much smaller than that generated by
Haar-like features. The training time is extremely short, and detection speed
and accuracy is similar to Haar-like feature based classifiers.
