EFFICIENT OBJECT DETECTION ROBUST TO RST WITH MINIMAL SET OF EXAMPLES
S. Onis, Henri Sanson, Christophe García, Jean‐Luc Dugelay · 2008
In this paper, we present an object detection approach based on a similarity measure combining crosscorrelation and affine deformation. Current object detection systems provide good results, at the expense of requiring a large training database. The use of correlation anables object detection with very small training set but is not robust to the luminosity change and RST (Rotation, Scale, translation) transformation. This paper presents a detection system that first searches the likely positions and scales of the object using image preprocessing and cross-correlation method and secondly, uses a similarity measure based on affine deformation to confirm or not the predetection. We apply our system to face detection and show the improvement in results due to the images preprocessing and the affine deformation.