Pose and position tracking with super image vector inner products

We introduce a new and efficient distortion-invariant super image tracker and pose estimator based on a linear phase coefficient composite filter. The super image consists of a weighted sum of training images chosen to span the distortion range under analysis. Unlike correlation-based composite filter design, the super image is implemented by means of a complex vector inner product operation. A super image vector inner product is implemented by elementwise multiplication of a super image template by a window of interest in the input scene and summation of the elementwise operations. The resulting amplitude indicates target detection, and the resulting phase indicates the value of scale, orientation, or movement of the target object. The mathematical characteristics of the super image vector inner product are presented, and its application is demonstrated. (c) 2006 Optical Society of America.