Authors:
Jiayin Liu
1
and
Yuan Wu
2
Affiliations:
1
DeepSea Precision Tech (Shenzhen) Co., Ltd. and Pusan National University, China
;
2
DeepSea Precision Tech (Shenzhen) Co., Ltd. and University of Bradford, China
Keyword(s):
Closed Contour Extraction, Structural Modelling, Likelihood Analysis, Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
In this paper, we describe a new algorithm for extracting closed contours inside images by introducing three basic structural models to describe all potentially closed contour candidates and their likelihood analysis to eliminate pixels of non-closed contours. To further enhance the performance of its closed contour extraction, a post processing method based on edge intensity analysis is also added to the proposed algorithm to reduce the false positives. To illustrate its effectiveness and efficiency, we applied the proposed algorithm to the casting defect detection problem and carried out extensive experiments organized in three phases. The results support that the proposed algorithm outperforms the existing representative techniques in extracting closed contours for a range of images, including artificial images, standard casting defect images from ASTM (American Society for Testing and Materials) and real casting defect images collected directly from industrial lines. Experimental
results also illustrate that the proposed algorithm achieve certain level of robustness in casting defect detection under noise environment.
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