Detecting gestures in medieval images

We present a template-based detector for gestures visualized in legal manuscripts of the Middle Ages. Depicted persons possess gestures with specific semantic meaning from the perspective of legal history. The hand drawn gestures exhibit noticeable variation in artistic style, size and orientation. They follow a distinct visual pattern, however, without any perspective effects. We present a method to learn a small set of templates representative of the gesture variability. We apply an efficient version of normalized cross-correlation to vote for gesture position, scale and orientation. Non-parametric kernel density estimation is used to identify hypotheses in voting space, and a discriminative verification step ranks the detections. We demonstrate our method on four types of gestures and show promising detection results.