Generation of continuous complex-valued functions for a joint transform correlator

Piestun, Rafael; Rosen, Joseph; Shamir, Joseph

doi:10.1364/ao.33.004398

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The direct and simple method is to use a transparency with the desired amplitude and phase modulations. Alternatively, if such transparency is not available, one can employ indirect methods such as holographic coding, 7 or interferometric techniques, 8 to get an effective complex reference function.…”

Section: Three-dimensional Correlator With a Virtual Reference Functionmentioning

confidence: 99%

Three-dimensional pattern recognition with a single two-dimensional synthetic reference function

Rosen

2000

Appl. Opt.

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Section: Three-dimensional Correlator With a Virtual Reference Functionmentioning

confidence: 99%

Three-dimensional pattern recognition with a single two-dimensional synthetic reference function

Rosen

2000

Appl. Opt.

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“…Some approaches that are based on representing complexvalued functions by their decomposition in three positive-valued components can overcome those drawbacks. 17 However, this process is difficult to implement with either coherent or incoherent illumination. It would be useful not only for optical techniques but also for digital techniques if a rotationinvariant real filter could be found.…”

Section: Experimental Systemmentioning

confidence: 99%

Nonlinear rotation-invariant pattern recognition by use of the optical morphological correlation

García‐Martínez¹,

Ferreira²,

Garcı́a³

et al. 2000

Appl. Opt.

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We introduce a modification of the nonlinear morphological correlation for optical rotation-invariant pattern recognition. The high selectivity of the morphological correlation is conserved compared with standard linear correlation. The operation performs the common morphological correlation by extraction of the information by means of a circular-harmonic component of a reference. In spite of some loss of information good discrimination is obtained, especially for detecting images with a high degree of resemblance. Computer simulations are presented, as well as optical experiments implemented with a joint transform correlator.

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“…3͒, as described in Ref. 25. In an interferometric configuration illuminated by a plane wave, the input pattern g and an SRF h i are positioned in the corresponding arms of the interferometer displaced from the axis by distances a and b, respectively.…”

Section: Correlator Architecturesmentioning

confidence: 99%

Adaptive pattern recognition correlators

Shamir

1997

Opt. Eng

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Optical pattern recognition is significantly improved by exploiting the attributes of both optics and electronics in architectures containing combinations of optical systems and electronic processors. Essential interfaces for such hybrid systems are spatial light modulators and optoelectronic recording devices. These devices suffer from technological limitations that must be alleviated by suitably designed optical architectures and efficient processing algorithms. We review several correlator architectures and discuss their adaptability to hybrid systems. Due to the computational complexity encountered in such hybrid electrooptical systems, iterative optimization methods can be efficiently employed. Some of the algorithms presented are especially useful for treating real physical systems having properties that cannot be exactly defined or quantified. Algorithms that were successful in these architectures belong to two families, the basically stochastic algorithms and the projection-onto-constraint-sets algorithms. The former is particularly suitable for applications in hybrid electro-optical learning systems, while the latter is extremely powerful for the design of spatial filters with properties that are difficult to achieve by other means. A case study is given of an adaptive correlator for rotation-, scale-, and shift-invariant pattern recognition.

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Generation of continuous complex-valued functions for a joint transform correlator

Cited by 6 publications

References 8 publications

Three-dimensional pattern recognition with a single two-dimensional synthetic reference function

Three-dimensional pattern recognition with a single two-dimensional synthetic reference function

Nonlinear rotation-invariant pattern recognition by use of the optical morphological correlation

Adaptive pattern recognition correlators

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