scholarly journals The incidence relation of an L-Fuzzy Context as structuring element in Fuzzy Mathematical Morphology

2013 ◽  
Author(s):  
Cristina Alcalde ◽  
Ana Burusco ◽  
Ramon Fuentes-Gonzalez
Keyword(s):  
Mathematical Morphology ◽  
Incidence Relation ◽  
Structuring Element ◽  
Fuzzy Mathematical Morphology

Application of Fuzzy Mathematical Morphology with Adaptive Structuring Elements to Seal Defect Testing

2002 ◽  
Vol 6 (1) ◽  
pp. 62-69
Author(s):  
Takuo Kikuchi ◽  
◽  
Shuta Murakami ◽  
Keyword(s):  
Mathematical Morphology ◽  
Input Image ◽  
Performance Evaluations ◽  
Automatic Inspection ◽  
Morphological Operators ◽  
Fuzzy Morphology ◽  
Structuring Element ◽  
Extraction Performance ◽  
Selection Of ◽  
Fuzzy Mathematical Morphology

Fuzzy mathematical morphology has been proposed as a new method of image processing, especially in the analysis of features from an ambiguous image. Fuzzy morphological operators work with 2 images: an original image to be processed and a structuring element. Generally, we must decide on the shape and value of the structuring element before applying fuzzy morphology. A problem arises when the applied image changes largely by the selection of the structuring element, so it is difficult to apply fuzzy morphological operators to inspection for defect testing. In this paper, we propose a new structuring element for fuzzy morphology, called an adaptive structuring element. The adaptive structuring element determines shapes and values of structuring elements dynamically from an input image by searching for the local features in the image. Consequently, the adaptive structuring element is sensitive to ambiguous images, useful for automatic inspection using fuzzy morphology, and effective for extraction. Performance evaluations via simulations show that the adaptive structuring element efficiently extracts features from an ambiguous image. The adaptive structuring element also shows a higher performance in package defect testing than other filters. We attained experimental results (more than 95.5%) by applying the adaptive structuring element to seal defect testing.

Compensatory fuzzy mathematical morphology

2017 ◽  
Vol 11 (6) ◽  
pp. 1065-1072 ◽  
Author(s):  
Agustina Bouchet ◽  
Juan I. Pastore ◽  
Marcel Brun ◽  
Virginia L. Ballarin
Keyword(s):  
Mathematical Morphology ◽  
Fuzzy Mathematical Morphology

Fuzzy mathematical morphology for color images defined by fuzzy preference relations

2016 ◽  
Vol 60 ◽  
pp. 720-733 ◽  
Author(s):  
Agustina Bouchet ◽  
Pedro Alonso ◽  
Juan Ignacio Pastore ◽  
Susana Montes ◽  
Irene Díaz
Keyword(s):  
Mathematical Morphology ◽  
Color Images ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference ◽  
Fuzzy Mathematical Morphology

A Survey Paper on: Fuzzy Mathematical Morphology Techniques for Digital Image Processing

2011 ◽  
Vol 403-408 ◽  
pp. 3469-3475 ◽  
Author(s):  
Gargi Aggarwal ◽  
Vijyant Agarwal
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Mathematical Morphology ◽  
Digital Image Processing ◽  
Digital Image ◽  
Medical Images ◽  
Spatial Relationships ◽  
Survey Paper ◽  
New Applications ◽  
Fuzzy Mathematical Morphology

This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation & Erosion, the structuring elements used in various techniques, the unique variations put forth by researchers, new applications in spatial relationships, decision making, segmentation of medical images have been discussed.

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