Distortion-tolerant minimum-mean-squared-error filter for detecting noisy targets in environmental degradation

Chan, F.H.Y.; Towghi, Nasser; Pan, Luting; Javidi, Bahram

doi:10.1117/1.1305543

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…16 Recently, composite optimum correlation filters for recognition of geometrically distorted objects embedded into a nonoverlapping background were also proposed. [17][18][19] Another fruitful approach to distortion-invariant pattern recognition is based on adaptive filters. [20][21][22] According to this concept, we are looking for a filter with good performance characteristics for a given set of observed scenes, i.e., with a fixed set of patterns and backgrounds to be rejected, rather than for a filter with average performance parameters over an ensemble of images.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Martínez-Díaz

Kober

2008

Opt. Eng

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Novel nonlinear adaptive composite filters for illuminationinvariant 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.

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Section: Introductionmentioning

confidence: 99%

Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Martínez-Díaz

Kober

2008

Opt. Eng

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Real-time Remote Identification and Verification of Objects Using Optical ID Tags

Javidi

Advanced Sciences and Technologies for Security Applications

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Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Díaz-Ramírez

Cuevas

Kober

et al. 2015

Optics Communications

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a b s t r a c tComposite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

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Distortion-tolerant minimum-mean-squared-error filter for detecting noisy targets in environmental degradation

Cited by 7 publications

References 17 publications

Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Nonlinear synthetic discriminant function filters for illumination-invariant pattern recognition

Real-time Remote Identification and Verification of Objects Using Optical ID Tags

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

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