Automated Region of Interest Retrieval of Metallographic Images for Quality Classification in Industry
|
| Title | Automated Region of Interest Retrieval of Metallographic Images for Quality Classification in Industry |
| Authors | |
| Abstract | The aim of the research is development and testing of new methods to classify the quality of metallographic samples of steels with high added value (for example grades X70 according API). In this paper, we address the development of methods to classify the quality of slab samples images with the main emphasis on the quality of the image center called as segregation area. For this reason, we introduce an alternative method for automated retrieval of region of interest. In the first step, the metallographic image is segmented using both spectral method and thresholding. Then, the extracted macrostructure of the metallographic image is automatically analyzed by statistical methods. Finally, automatically extracted region of interests are compared with results of human experts. Practical experience with retrieval of non-homogeneous noised digital images in industrial environment is discussed as well. |
| Publisher | Faculty of Electrical Engineering and Computer Science |
| Date | 2012-03-31 |
| Source | Advances in Electrical and Electronic Engineering Vol 10, No 1 (2012): March |
| Rights | Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work´s authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal´s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). |