Fuzzy mathematical morphology for color images defined by fuzzy preference relations

Bouchet, Agustina; Alonso, Pedro; Pastore, Juan Ignacio; Montes, Susana; Dı́az, Irene

doi:10.1016/j.patcog.2016.06.014

Cited by 24 publications

(22 citation statements)

References 25 publications

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“…The color vectors were sorted by their distance to the black and white pixels respectively. Bouchet et al [32] used a fuzzy order based on fuzzy preference relation to create a total order complete lattice in RGB color space. The three fuzzy preference relations were then aggregated with the arithmetic mean.…”

Section: B Color Mathematical Morphologymentioning

confidence: 99%

Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

2021

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In this paper, the definition of three-dimensional generalized discrete fuzzy number (3-GDFN) is introduced based on the representation theorem of one-dimensional discrete fuzzy number and the similarity measure definition of two 3-GDFNs is given. Then the concept above mentioned is applied to color image representation and color mathematical morphology (CMM) in RGB space. The basic morphology operators, erosion and dilation, are extended to the CMM by defining the total preorder relation based on similarity measure between two 3-GDFNs instead of general vector sorting methods. The corresponding structuring elements in CMM are variable. Finally, the effectiveness and potential of the theoretical results are verified by comparative experiments. The proposed CMM operators are efficiently used in color image processing. INDEX TERMS color mathematical morphology, generalized discrete fuzzy numbers, RGB color space, similarity degree, three-dimensional fuzzy numbers.

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Section: B Color Mathematical Morphologymentioning

confidence: 99%

Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

2021

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“…Mathematical morphology is a nonlinear filtering method, which generally includes erosion, dilation, open operation, and close operation [18][19][20]. Mathematical morphology uses mathematical tools to process image structure components.…”

Section: Image Optimizationmentioning

confidence: 99%

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Gao

et al. 2020

Agronomy

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Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

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“…Fuzzy Mathematical Morphology [5,44] is an extension of the Mathematical Morphology's binary operators to gray level images, by redefining the set operations as fuzzy set operations. In [7,8,9] we define the operators of the Fuzzy Mathematical Morphology for color images through the use of a fuzzy order. Other important works related to image processing are highlighted: In [16] information about spatial organization in an image is considered to improve object recognition and scene analysis tasks.…”

Section: Applicationsmentioning

confidence: 99%

Some Results and Applications Using Fuzzy Logic in Artificial Intelligence

Dı́az

Ralescu

et al. 2018

Studies in Systems, Decision and Control

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Algunas veces encuentras en la vida una amistad especial: ese alguien que al entrar en tu vida la cambia por completo. M. Benedetti, "Algunas amistades"Abstract In this chapter, several results and applications in artificial intelligence based on fuzzy logic are presented. We aim to highlight some recent works that are connected with the topic of this book, both in the theoretical and in the applied fields.

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Fuzzy mathematical morphology for color images defined by fuzzy preference relations

Cited by 24 publications

References 25 publications

Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

Three-Dimensional Generalized Discrete Fuzzy Number and Applications in Color Mathematical Morphology

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Some Results and Applications Using Fuzzy Logic in Artificial Intelligence

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