Recently, nonlinear correlation filters have been proposed for distortion-invariant pattern recognition. The design of the
filters is based on rank-order, logical operations and nonlinear correlation. These kinds of filters are robust to non
Gaussian noise and non-homogeneous illumination. A drawback of nonlinear filters is its high computational cost;
however, the computation of nonlinear correlation can be parallelized. In this paper a hardware implementation of
nonlinear filtering is presented. The hardware coprocessor is based on a Field Programmable Gate Array (FPGA) device.
Simulation results are provided and discussed.
Correlation-based pattern recognition has been an area of extensive research in the past few decades. Recently,
composite nonlinear correlation filters invariants to translation, rotation, and scale were proposed. The design of the
filters is based on logical operations and nonlinear correlation. In this work nonlinear filters are designed and applied to
non-homogeneously illuminated images acquired with an optical microscope. Images are embedded into cluttered
background, non-homogeneously illuminated and corrupted by random noise, which makes difficult the recognition task.
Performance of nonlinear composite filters is compared with performance of other composite correlation filters, in terms
discrimination capability.
Classical correlation-based methods for pattern recognition are very sensitive to geometrical distortions of objects to be
recognized. Besides, most captured images are corrupted by noise. In this work we use novel nonlinear composite filters
for distortion-invariant pattern recognition. The filters are designed with an iterative algorithm to reject a background
noise and to achieve a desired discrimination capability. The recognition performance of the proposed filters is compared
with that of linear composite filters in terms of noise robustness and discrimination capability. Computer simulation
results are provided and discussed.
Novel nonlinear adaptive composite filters for illumination-invariant pattern recognition are presented. Pattern recognition is carried out with space-variant nonlinear correlation. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in nonuniformly illuminated test scenes are discussed and compared with those of linear composite correlation filters with respect to discrimination capability, robustness to input additive and impulsive noise, and tolerance to small geometric image distortions.
In this paper, adaptive nonlinear correlation-based filters for pattern recognition are presented. The filters are based on a
sum of minima correlations. To improve the recognition performance of the filters in presence of false objects and
geometric distortions, information about the objects is used to synthesize the filters. The performance of the proposed
filters is compared to that of the linear synthetic discriminant function filters in terms of noise robustness and
discrimination capability. Computer simulation results are provided and discussed.
In this paper, we use adaptive rank-order filters for localization and extraction of desirable details from images. To
improve the performance of linear correlations, we use novel local adaptive correlations based on nonparametric
Spearman's correlation. These filters are based on correlation between the ranks of the input scene computed in a
moving window and those of the target. Their performance and noise robustness are compared with those of the
conventional linear correlation. Computer simulation results are provided and discussed.
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