Abstract

We propose an invariant adaptive technique for automated segmentation of a target-and-background frame from an optoelectronic device for detection of dynamic objects in the image. The technique involves performing a wavelet transform on the image such that threshold processing of wavelet coefficients is optimum (in the sense of the Neyman–Pearson principle) based on a very powerful local unbiased test, and does not require any a priori data on the target environment, any reference images of the dynamic objects, or the locations and dimensions of the windows used for object detection. This is implemented solely using the information contained in images recorded by the optoelectronic device. We present an algorithm and results from an assessment of segmentation quality statistics for non-steady-state (and steady-state) images under various operating conditions. The technique described in this paper is found to be highly efficient and can be implemented as a real-time algorithm.

© 2016 Optical Society of America

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