An Approach Towards Image Edge Detection Based on Interval-Valued Fuzzy Mathematical Morphology and Admissible Orders

Sussner, Peter; Carazas, Lisbeth Corbacho

doi:10.2991/eusflat-19.2019.96

Cited by 5 publications

(9 citation statements)

References 13 publications

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“…With the increasingly high requirements of modern industry on the measurement accuracy of various workpiece and parts, target detection for precision parts and high-precision measuring equipment is particularly important. PeterSussner, LisbethCorbachoCarazas et image detection based on interval value fuzzy mathematical morphology research [1] the mathematics method and the image detection together. At the same time, Hui Zhang et al [2] also mentioned the application of computer vision to the detection and positioning of electric vehicle charging holes, proving that image pro-cessing is very necessary in the detection of industrial precision parts.…”

Section: Introductionmentioning

confidence: 99%

Research on Image Contour Edge Analysis Based on Canny Edge Detector

2022

AJCIS

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In this paper, aiming at the sub-pixel edge contour detection method, the sub-pixel edge contour extraction model and camera imaging geometric model are established step by step. Firstly, the improved anti-tangent function model based on canny operator is adopted. Firstly, the image is grayed out, then the edge of canny operator is detected by Gaussian filter, and a better method of edge detection after extended smoothing is proposed. Extract the sub-pixel edge outline of the target, then detect and fit lines, curves and circles, and mark them with different colors. Then for the correction of the image captured by the camera, this paper uses an image calibration model based on Zhang Zhengyou camera calibration method. The camera is calibrated by the calibration picture given, the internal and external parameters of the camera are obtained, and the unit matrix is calculated. Using the given definition of calibration board information, the number of pixels per millimeter pixel in the image is estimated. The research is helpful to improve the accuracy of sub-pixel edge contour detection and improve the image edge detection of industrial parts to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Research on Image Contour Edge Analysis Based on Canny Edge Detector

2022

AJCIS

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“…Then we adopt two different strategies: The first one consists in computing the image whose values are given by the centers of the intervals, which amounts to preordering the intervals using an h-order [14], before processing it further using a fuzzy morphological gradient and a watershed transform [20]. The second strategy includes the computation of a morphological gradient using interval-valued fuzzy morphological operators [24,30,31].…”

Section: Introductionmentioning

confidence: 99%

Some Approaches Based on Interval-Valued Images and L-Fuzzy Mathematical Morphology for Segmenting Images Reconstructed from Noisy Sinograms

Sussner

Carazas²,

Miqueles³

2021

Atlantis Studies in Uncertainty Modelling

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Two-dimensional image reconstruction from projections is a well known research field with different applications and using different sources, e.g., varying from medical imaging to a synchrotron laboratory. In the mathematical sense, we are looking for a two dimensional piecewise function with compact support for which a set of signals -the measurements, so called projections or sinograms -are known a priori. After solving the corresponding inverse problem using an appropriate numerical scheme, a collection of reconstructed (gray-scale) images are provided for the final user for further analysis. In practice, the sinogram is often affected by various source of noise which lead to artefacts in the reconstructed images.In this paper, we first convert the resulting gray-scale image that can be viewed as an L-fuzzy set, where L is a finite chain, into an interval-valued image whose values are non-empty, closed intervals of L, so as to express the uncertainty about its values. Subsequently, we apply four approaches of morphological image segmentation, all of which make use of the interval values of the image. Each of these approaches employs some type of morphological gradient and the watershed algorithm. We only consider transmission sinograms for this paper, as emission problems are beyond the scope of this paper.

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“…Existem diversos métodos de detecção de bordas, dentre os quais destacam-se alguns métodos clássicos (como Canny [4], Roberts [5], Sobel [6], gradiente morfológico [7]) e os métodos baseados em sistemas fuzzy tipo-1 [8,9], em sistemas fuzzy tipo-2 [10][11][12], em teoria de conjuntos fuzzy [13], em teoria de conjuntos fuzzy intervalar [14,15], etc. Embora existam diversas técnicas para a extração da borda de uma imagem, os detectores de bordas baseados em morfologia matemática (MM) fuzzy e suas extensões estão dando resultados destacáveis [16][17][18][19][20].…”

Section: Introductionunclassified

“…Apesar que as imagens digitais podem ser processadas utilizando a MM fuzzy discreta, observa-se que tais imagens apresentam certo grau de incerteza. Essa incerteza pode ser observada tanto na posição de seus pixeis quanto na sua tonalidade [18,20,33]. Lopez-Molina et al [33] propuseram um modelo de captura dessa incerteza obtendo imagens digitais intervalares representadas por funções Z 2 Ñ I ¦ n .…”

Section: Introductionunclassified

“…Determinar as bordas das imagens digitais intervalares utilizando ferramentas da MM I ¦ n -fuzzy pode levar a outros problemas, pois métodos existentes de afinamento e binarização (para obtenção da borda da imagem de acordo com as condições de Canny) servem apenas para imagens digitais e não para imagens digitais intervalares. Diante disso, no Capítulo 4 do presente trabalho, são propostas duas abordagens de redução de uma imagem digital intervalar numa imagem digital por meio de h-ordens, que incluem as ordens admissíveis, sendo uma a priori e outra a posteriori da utilização da MM L-fuzzy (ao respeito disso, alguns resultados parciais já têm sido publicados [20,34]).…”

Section: Introductionunclassified

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Detecção de bordas de imagens digitais baseada em morfologia matemática L-fuzzy e h-Ordens

Corbacho Carazas

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AgradecimentosO presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Código de Financiamento 001, e com apoio do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), número de processo 142461/2018-0.Minha imensa gratidão ao meu orientador, professor Dr. Peter Sussner, por toda a dedicação, orientação, pelas discussões acadêmicas e pelos desafios colocados, os quais foram fundamentais para a concretização desta tese e o meu crescimento profissional.

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An Approach Towards Image Edge Detection Based on Interval-Valued Fuzzy Mathematical Morphology and Admissible Orders

Cited by 5 publications

References 13 publications

Research on Image Contour Edge Analysis Based on Canny Edge Detector

Research on Image Contour Edge Analysis Based on Canny Edge Detector

Some Approaches Based on Interval-Valued Images and L-Fuzzy Mathematical Morphology for Segmenting Images Reconstructed from Noisy Sinograms

Detecção de bordas de imagens digitais baseada em morfologia matemática L-fuzzy e h-Ordens

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