A Review of Optical Pattern Recognition Techniques

Ullmann, J. R.

doi:10.1007/978-1-4613-4154-3_2

Cited by 8 publications

(2 citation statements)

References 87 publications

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“…When one of the patterns is a standard, the transformation process is sometimes referred to as normalization. The transformations may be applied before the correlation operation according to a prescribed procedure, or contemporaneously with the correlation operation to maximize the coincidence measure (Marko, 1973 ;Ullmann, 1974).…”

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Transformation and relational-structure schemes for visual pattern recognition

Foster

Mason

1979

Biol. Cybernetics

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Abstract. Two models for visual pattern recognition are described ; the one based on application of internal compensatory transformations to pattern representations, the other based on encoding of patterns in terms of local features and spatial relations between these local features. These transformation and relational-structure models are each endowed with the same experimentally observed invariance properties, which include independence to pattern translation and pattern jitter, and, depending on the particular versions of the models, independence to pattern reflection and inversion (180" rotation). Each model is tested by comparing the predicted recognition performance with experimentally determined recognition performance using as stimuli random-dot patterns that were variously rotated in the plane. The level of visual recognition of such patterns is known to depend strongly on rotation angle. It is shown that the relationalstructure model equipped with an invariance to pattern inversion gives responses which are in close agreement with the experimental data over all pattern rotation angles. In contrast, the transformation model equipped with the same invariances gives poor agreement to the experimental data. Some implications of these results are considered. mations, for example, translations and dilatations.

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Transformation and relational-structure schemes for visual pattern recognition

Foster

Mason

1979

Biol. Cybernetics

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“…Of the many text books in the field, I give here six in their order of relevance for high-energy physicists: Andrews (1972), Young and Calvert (1974), Reingold et a1 (1977), Tou and Gonzalez (1974), Patrick (1972) and Ullmann (1973).…”

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confidence: 99%

Pattern recognition in high-energy physics

Grote

1987

Rep. Prog. Phys.

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Pattern recognition is of crucial importance to many high-energy physics experiments during their analysis phase. This review gives a short introduction to those aspects of experiments that require the application of pattern recognition methods, which are discussed in detail and illustrated with examples from high-energy physics experiments of the last 15 years. At the end, a number of papers are recommended dealing with track and vertex fitting.

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Pattern recognition using one-dimensional fourier transformation

Almi

Shamir

1976

Optics Communications

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A Review of Optical Pattern Recognition Techniques

Cited by 8 publications

References 87 publications

Transformation and relational-structure schemes for visual pattern recognition

Transformation and relational-structure schemes for visual pattern recognition

Pattern recognition in high-energy physics

Pattern recognition using one-dimensional fourier transformation

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