Katarzyna Chalasinska-Macukow

Optoelectronic implementation of the quasi-phase correlator

Abstract Optoelectronic implementation of the quasi-phase correlator based on the joint transform correlator architecture is proposed. Both digital and optical results of recognition are presented and discussed.

Optical correlator with dual nonlinearity

Abstract A generalization of the main linear and nonlinear correlation methods, the dual nonlinear correlation (DNC), is introduced. Various filters (CMF, POF, IF, FPF, PPC, OF) appear to be special cases of the DNC. The convolution formula describing the peak shape and the intermodulation effects for the noise-free multi-object scenes is discussed. Optimization of the DNC according to the peak-to-correlation energy criterion is performed. Superior performance of the PPC is presented. The influence of noise on the intermodulation effects is studied. Hybrid, optoelectronic realization of the DNC based on joint transform correlator architecture with nonlinear preprocessing which allows the introduction of various correlation methods without any fiters is proposed.

Nonlinearity effects in the pure phase correlation method in multiobject scenes

The nonlinearity used in the pure phase correlation method can improve pattern recognition, but it causes high-order harmonics in the correlation plane in the case of multiobject scenes. High-order harmonics and the presence of aliasing may cause false alarms. We show that these effects are strongly diminished when the periodicity in the scene is broken (by different objects, random distribution, etc.).

Processing measurements of the directional content of Fourier spectra.

Directional analysis and filtering make use of the most straightforward properties of optical Fourier transforms. A quantitative study of the detectability of angular maximums in Fourier spectra and its relationships with the shape and distribution of elements in the object is proposed. The problem is modeled by using a simple object which consists of the superimposition of two random distributions of rectangular grains, one being rotated with respect to the other. Computing the Fourier spectrum of this object allows the expression of the amount of light integrated through a wedge which scans the spectrum. An index of angular separability of the two distributions is made up from such measurements. It is shown to depend on the rotation angle as well as the grain shape. Using an additional spatial filter can improve this index. The influence of its radius is studied. Experimental results obtained either in real time with optical fibers or on photographic records with a microdensitometer are compared with the theoretical ones. They show the necessity of the implementation of optimal estimation schemes to reduce the influence of the noise. Two linear least squares filters are used: an angular Wiener filter and an autoregressive one. In the case of a high signal-to-noise ratio, the Wiener filter reduces to a Laplacian filter which does not depend on the shape of the grains.

Recognition of partially occluded objects by correlation methods

Abstract Different methods proposed for pattern recognition are applied to discriminate occluded objects. The phase-only filter, the inverse filter and the minimum variance — minimum average correlation energy filter are considered. Numerical and optical results of the recognition are presented.

Optical pattern recognition with adjustable sensitivity to shape and texture

In this paper, an optical pattern recognition system with adjustable sensitivity to shape distortions and texture changes of the objects is presented. Application to a recognition task where the information of texture is the most decisive feature for a given object to be detected is provided. We apply the dual nonlinear correlation (DNC) model along with a support function acting in the frequency domain. This support function performs as an additional nonlinearity that enhances the information of some selected frequency bands related to the textural content of the target. A mathematical analysis allows the authors to show the usefulness of the proposed support function in the frame of the DNC model. The recognition system is applied to accomplish different recognition tasks involving model and real textured objects. The proposed optoelectronic correlator has been used to obtain successful experimental optical results, which are in accordance with the simulated results also provided.

New arrangement for limited intensity invariance pattern recognition with high diffraction efficiency

A comparison between correlations obtained with amplitude and phase encoded scenes is presented. We demonstrate a better diffraction efficiency and a greater discrimination capability for the setup with phase encoded scenes. We report the limited intensity invariance characteristic of the new arrangement. Numerical and optical recognition results are shown.

Accuracy of angular spectral analysis with an anamorphic Fourier transformer

Angular measurements in symmetrical and nonsymmetrical Fourier spectra are compared. The coefficient of angular magnification of a spectrum and the effective angular extent of a scanning wedge filter are introduced. Better accuracy of angular spectral analysis with an anamorphic Fourier transformer is explained and experimentally proved.

Pattern recognition with variable discrimination capability by dual non-linear optical correlation

The discrimination capability requirements of pattern recognition systems may vary from one given purpose to another. In this work a recognition system with variable and selective discrimination capability is obtained by applying a dual non-linear correlation (DNC) model to a joint-transform correlator. DNC is obtained by means of two non-linear operators that are applied to both the reference and input channels. A particular DNC is given by the values taken by two real control parameters that determine the non-linear operators. In comparison with conventional filtering methods, an increased and variable discrimination capability is achieved by varying the parameters values. Thus, variable tolerances are introduced in the recognition process. Specifically, tolerances to slight shape variations and intensity variations of the objects (alphabetic characters) are analysed in this work. Ranges for the two control parameters are found in each case in order to achieve either an increase or a relaxation in the systems discrimination capability. The developed application is extended to colour pattern recognition by multichannel correlation. In this case, four further applications with selective discrimination capability are developed: pattern recognition with high discrimination capability for shape variations and some tolerance to colour variations and, vice versa, pattern recognition with high discrimination capability for colour variations and some tolerance to slight shape variations; pattern recognition with high discrimination for both shape and colour, and, finally, a tolerance to slight variations in both shape and colour.

Wavelet transform for preprocessing in an optical correlator with a multilevel composite filter

A technique of optical pattern recognition that combines the performance of a wavelet transformation and a multilevel composite filter is proposed. The essential advantage of this technique is distortion invariance. We show by digital simulation that this technique can successfully identify and discriminate complex biometric images, such as fingerprints distorted digitally by various types of distortions: rotation (up to 10 deg clockwise and counterclockwise), shift (up to 10% of the image size), occlusion, scaling (changes up to 5%), and pinch and punch (changes up to 10%). The wavelet transform is used for extracting crucial ridge information from tested images and eliminating its redundancies. The multilevel composite filter is generated by a simulated annealing algorithm.©