Pattern Recognition Using Photon-Limited Images

摘要

The spatial coordinates of detected photoevents and the number of detected photoevents in a given area convey information about the classical irradiance of the input scene. In this paper the effectiveness of photon-counting techniques for image recognition is discussed. A correlation signal is obtained by cross correlating a photon-limited input scene with a classical intensity reference function stored in computer memory. Laboratory experiments involving matched filtering, rotation- and scale-invariant image recognition, and image classification are reported. For many images it is found that only a sparse sampling of the input is required to obtain accurate recognition decisions, and the digital processing of the data is extremely efficient. Using available photon-counting detection systems, the total time required to detect, process, and make a recognition decision is typically on the order of tens of milliseconds. This work has obvious applications in night vision, but it is also relevant to areas such as process control, radiological, and nuclear imaging, spectroscopy, robot vision, and vehicle guidance.