Vectorial signatures for invariant recognition of position, rotation and scale pattern recognition

Aragón, Jesús; Álvarez-Borrego, Josué

doi:10.1080/09500340903203111

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…Unfortunately, these filters are specialized to solve specific problems, for example one filter could has an excellent performance in the discrimination step and the signal-to-noise ratio but a low e ciency under non-homogeneous illumination [2][3][4] . Although composite filters has been used, the problem of the invariant to rotation, scale and translation (RST) correlator image descriptor is an active field of study due to its intrinsic complexity [5][6][7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition descriptor using the Z-Fisher transform

Barajas-García

Solorza-Calderón

Álvarez-Borrego

2015

Applications of Digital Image Processing XXXVIII

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In this work is presented a pattern recognition image descriptor invariant to rotation, scale and translation (RST), which classify images using the Z-Fisher transform. A binary rings mask is generated using the Fourier transform. The normalized analytic Fourier-Mellin amplitude spectrum is filtered with that mask to build 1D signature. The signatures comparison of the problem image and the target are done by the Pearson correlation coe cient (PCC). In general, those PCC values do not satisfy a normal distribution, hence the Fisher's Z distribution is employed to determine the confidence level of the RST invariant descriptor. The descriptor presents a confidence level of 95%.

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Section: Introductionmentioning

confidence: 99%

Pattern recognition descriptor using the Z-Fisher transform

Barajas-García

Solorza-Calderón

Álvarez-Borrego

2015

Applications of Digital Image Processing XXXVIII

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“…In the last years, the methods of correlation are the most used techniques in a wide variety of areas [10][11][12][13][14][15][16][17] . Recognition systems have achieved a high degree of reliability in objects recognition in addition to a low computational cost [18][19] .…”

Section: Introductionmentioning

confidence: 99%

Invariant correlation by using vectorial signatures and spectral index

2011

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In this paper a non-linear correlation methodology to recognize objects is used. This new system is invariant to position, rotation and scale. This digital system has a low computational cost to achieve a significant reduction of processed information by using vectorial signatures. The invariant vectorial signatures are obtained from the information from both the target image as well as problem image. In this way, each image has its rotational and scale vectorial signature obtained through several mathematical transformations such as scale and Fourier transform. So, this method uses the great capacities from the non-linear filters to discriminate between similar objects. Vectorial signatures are compared using non-linear correlation. The result of this comparison is shown in a bi-dimensional plane where the x axis is the result of the rotation correlation and the y axis is the result of the scale correlation. In addition, spectral index and vectorial signature index are obtained through several mathematical transformations in order to recognize the objects in a more simple way. 21 different fossil diatoms images were used. The results obtained are analyzed and discussed.

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“…In recent years, the methods of correlation are some of the most used techniques in a wide variety of areas (Álvarez-Borrego et al 2002;Álvarez-Borrego & Castro-Longoria 2003;Mouriño-Pérez et al 2006;Álvarez-Borrego & Fájer-Ávila 2006;Solorza & Álvarez-Borrego 2009Lerma-Aragón & Álvarez-Borrego 2009;Hernández et al 2010;Coronel-Beltrán & Álvarez-Borrego 2010). Also, these kinds of systems have been achieved that in addition to its high degree of reliability in objects recognition have low computational cost (Lerma-Aragón & Álvarez-Borrego 2009;Bueno-Ibarra et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Also, these kinds of systems have been achieved that in addition to its high degree of reliability in objects recognition have low computational cost (Lerma-Aragón & Álvarez-Borrego 2009;Bueno-Ibarra et al 2010). …”

Section: Introductionmentioning

confidence: 99%

Nonlinear correlation by using invariant identity vectors signatures to identify plankton

Fimbres-Castro¹,

Álvarez-Borrego

Vázquez-Martínez³

et al. 2013

Gayana (Concepc.)

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In this paper a new methodology to recognize radiolarians is presented. This system is invariant to position, rotation and scale by using identity vectors signatures (I s ) obtained for both the target and the problem image. In this application, I s is obtained by means of a simplifi cation of the main features of the original image in addition of the properties of the Fourier transform. Identity vectors signatures are compared using nonlinear correlation. This new methodology recognizes objects in a more simple way. It has a low computational cost of approximately 0.02 s per image. In addition, the statistics of Euclidean distances is used as an alternative methodology for comparison of the identity vectors signatures. Also, experiments were carried out in order to fi nd the noise tolerance. The discrimination coeffi cient was used as a metric in performance evaluation in presence of noise. The invariant to position, rotation and scale of this digital system was tested with 20 different species of radiolarians and with 26 different species of phytoplankton (real images). The results obtained have a confi dence level above 95.4%.KEYWORDS: image processing, invariant digital system, pattern recognition, plankton identifi cation. RESUMENEn este trabajo se presenta una nueva metodología para el reconocimiento de radiolarios. Este sistema es invariante a posición, rotación y escala y utiliza fi rmas vectores identidad (I s ) tanto como para la imagen problema como para la imagen objetivo. En esta aplicación, I s se obtiene mediante una simplifi cación de los rasgos totales de la imagen original tomando en cuenta las propiedades de la transformada de Fourier. Los vectores identidad son comparados entre sí mediante una correlación no lineal. Esta nueva metodología reconoce objetos de manera muy sencilla. Tiene un costo bajo computacional de aproximadamente 0.02 s por imagen. Además, de una manera alternativa se utiliza la estadística de distancia Euclidiana para comparar los vectores identidad. Se llevaron a cabo experimentos numéricos para encontrar la tolerancia del método al ruido en la imagen. La métrica matemática coefi ciente de discriminación fue utilizada para evaluar el desarrollo del algoritmo en presencia de ruido. El sistema digital invariante a posición, rotación y escala fue evaluado con 20 diferentes especies de radiolarios y con 26 diferentes especies de fi toplancton (imágenes reales). Los resultados obtenidos tuvieron un nivel de confi anza del 95.4%.PALABRAS CLAVE: procesado de imágenes, sistema digital invariante, reconocimiento de patrones, identifi cación de plancton.

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Vectorial signatures for invariant recognition of position, rotation and scale pattern recognition

Cited by 14 publications

References 15 publications

Pattern recognition descriptor using the Z-Fisher transform

Pattern recognition descriptor using the Z-Fisher transform

Invariant correlation by using vectorial signatures and spectral index

Nonlinear correlation by using invariant identity vectors signatures to identify plankton

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