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We present in detail the recorded results of the modified-hybrid optical neural network (M-HONN) filter during a full series of tests to examine its robustness and overall performance for object recognition tasks. We test the M-HONN filter for its detectability and peak sharpness with within-class distortion of the input object, its discrimination ability between an in-class and out-of-class object, and its performance with cluttered images of the true-class object. The M-HONN filter is found to exhibit good detectability, an ability to maintain its correlation-peak sharpness throughout the recorded tests, good discrimination ability, and an ability to detect the true-class object within cluttered input images. Additionally we observe the M-HONN filter's performance within the tests in comparison with the constrained-hybrid optical neural network filter for the first three series of tests and the synthetic discriminant function-maximum average correlation height filter for the fourth set of tests. OCIS codes: 030.1640, 070.4550, 100.6740, 130.4310, 100.5760, 200.4260.

Real-time correction of an optically aberrated wave front by use of a 10 x 10 ferroelectric liquid-crystal spatial light modulator as the correction device and a point-diffraction interferometer as the wave-front sensor is demonstrated. This type of interferometer requires no reference arm and so can be used, in theory, in an astronomical adaptive-optics system. We discuss some of the unusual features of the point-diffraction interferometer for wave-front sensing. OCIS codes: 160.3710, 010.1080, 120.3180, 230.6120.