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

Hamiltonian Streamline Guided Feature Extraction with Applications to Face Detection