Real-time pattern recognition with adaptative correlation filters

One of the most important performance measures for pattern recognition systems is the discrimination capability, or how well a system can recognize objects of interest as well as reject wrong objects and the background. In real time opto-digital pattern recognition systems the light efficiency (how much of the input light energy passes trough the system) and parameters of optical setup should also taken into account. In this work we propose a new adaptive composite correlation filters based on synthetic discriminant functions, which are able to improve significantly the discrimination capability and which yield a high light efficiency. An iterative design procedure is used for the digital design of phase-only filter. A desired value of the discrimination capability is obtained by exploiting information about the target signal, background, and objects to be rejected. Next the designed filter with a high light efficiency is implemented in an optical setup. We use real scenes to test the proposed filter in a real-time opto-digital system. Experimental results obtained in the system are compared with those obtained with computer simulation.