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

Author(s):  
Takuo Kikuchi ◽  
◽  
Shuta Murakami ◽  

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.

2014 ◽  
Vol 44 (3) ◽  
pp. 231-236
Author(s):  
A. BOUCHET ◽  
F. BENALCÁZAR PALACIOS ◽  
M. BRUN ◽  
V. L. BALLARIN

 Despite a large amount of publications on Fuzzy Mathematical Morphology, little effort was done on systematic evaluation of the performance of this technique. The goal of this work is to compare the robustness against noise of Fuzzy and non Fuzzy Morphological operators when applied to noisy images. Magnetic Resonance Images (MRI) of the brain are a kind of images containing some characteristics that make fuzzy operators an interesting choice, because of their intrinsic noise and imprecision. The robustness was evaluated as the degree in which the results of the operators are not affected by artificial noise in the images. In the analysis we compared different implementation of Fuzzy Mathematical Morphology, and observed that in most of the cases they show higher robustness against noise than the classical morphological operators.


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

2014 ◽  
Vol 31 (7) ◽  
pp. 1221-1241 ◽  
Author(s):  
Rubén Sarabia-Pérez ◽  
Antonio Jimeno-Morenilla ◽  
Rafael Molina-Carmona

Purpose – The purpose of this paper is to present a new geometric model based on the mathematical morphology paradigm, specialized to provide determinism to the classic morphological operations. The determinism is needed to model dynamic processes that require an order of application, as is the case for designing and manufacturing objects in CAD/CAM environments. Design/methodology/approach – The basic trajectory-based operation is the basis of the proposed morphological specialization. This operation allows the definition of morphological operators that obtain sequentially ordered sets of points from the boundary of the target objects, inexistent determinism in the classical morphological paradigm. From this basic operation, the complete set of morphological operators is redefined, incorporating the concept of boundary and determinism: trajectory-based erosion and dilation, and other morphological filtering operations. Findings – This new morphological framework allows the definition of complex three-dimensional objects, providing arithmetical support to generating machining trajectories, one of the most complex problems currently occurring in CAD/CAM. Originality/value – The model proposes the integration of the processes of design and manufacture, so that it avoids the problems of accuracy and integrity that present other classic geometric models that divide these processes in two phases. Furthermore, the morphological operative is based on points sets, so the geometric data structures and the operations are intrinsically simple and efficient. Another important value that no excessive computational resources are needed, because only the points in the boundary are processed.


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