Optical implementation of the weighted sliced orthogonal nonlinear generalized correlation for nonuniform illumination conditions

García‐Martínez, Pascuala; Tejera, Manuel; Ferreira, Carlos; Lefebvre, Daniel; Arsenault, Henri H.

doi:10.1364/ao.41.006867

Cited by 21 publications

(15 citation statements)

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“…This correlation has many properties as explained in Ref. [24]. For instance, it measures the number of common pixels between the two functions for any gray level.…”

Section: Nonlinear Correlations Using Phase Input Codificationmentioning

confidence: 99%

“…We use the addition of many linear correlations between the elements of a binary decomposition of the input scene and of the reference object. They can be implemented optically using a time sequential joint transform correlator (JTC) for adding the joint power spectra of all the binary slices 23,24 . In this paper we use a phase-transformed input joint transform correlation to implemented optically the nonlinear correlation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear optical time sequential correlations using phase input encoding

et al. 2004

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The presentation of nonlinear optical correlations using a joint transform correlator operating in phase-only spatial light modulation at input joint transform plane is studied here. Amplitude input nonlinear optical time sequential correlations have better discrimination and noise robustness than conventional linear correlations. These nonlinear correlations are based on decomposing reference and target into binary slices and adding the contribution of all linear correlations between them. Those correlations are implemented using a conventional joint transform correlator. However as the system has poor efficiency and low cross-correlation peak intensity working in amplitude mode, we use a phasetransformed input joint transform correlator in order to increase that efficiency and discrimination. We implement optically the phase morphological correlation and the phase sliced orthogonal nonlinear generalized correlation. We have applied the method to images degraded with high levels of substitutive noise and nonoverlapping background noise. The obtained results show how those nonlinear phase input encoded correlations have high discrimination capability detecting the target in cases where other well-known methods fail.

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“…This correlation has many properties as explained in Ref. [24]. For instance, it measures the number of common pixels between the two functions for any gray level.…”

Section: Nonlinear Correlations Using Phase Input Codificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear optical time sequential correlations using phase input encoding

et al. 2004

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show abstract

“…On the other hand, several approaches of nonlinear filter design have been proposed too [7,8,9]. Recently, nonlinear composite filters for distortion-invariant pattern recognition were introduced [10].…”

Section: Introductionmentioning

confidence: 99%

Rotation-Invariant Nonlinear Filters Design

Martínez-Díaz¹,

Martínez-Chavelas

2014

Advanced Information Systems Engineering

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Abstract. Geometrical distortions are a major problem in image recognition. Composite correlation filters can be used for distortion-invariant image recognition by incorporating rotated versions of the target object. Traditionally composite filters are designed with linear techniques; but, these filters are sensitive to non-Gaussian noise. On the other hand, for the same purpose, composite nonlinear filters have been proposed too. These filters have a good discrimination capability and they are robust to non-Gaussian noise and illumination changes; however, the performance of filter could be reduced when the number of training images incorporated increases. In this paper, we propose a method for designing rotation-invariant composite nonlinear filters. The method tries to maximize the number of objects incorporated into the filter and preserve its performance.

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“…These degradations [5]- [8] are caused by nonuniform illumination, imperfections in the manufacturing process of sensors (i.e. Focal Plane Array), damaged sensors, etc.…”

Section: Introductionmentioning

confidence: 99%