GRNTI 50.07 Теоретические основы вычислительной техники
BBK 3297 Вычислительная техника
Analysis of metals' microstructure images is an actual quantitative analysis problem, solved by quality control and research labs in the field of metallurgy. SIAMS Ltd pursues the goal of improving microstructure analysis quality, speed, and convenience. This article discusses the issues of recognition of the microstructure elements of metals and alloys, the most common problems and recognition errors, methods for solving them. Microstructure examples are given before and after digital image processing. The question of the advisability of using server technology in digital microscopy that removes such restrictions from users as the size of the shooting area and the area of microstructure analysis, binding to one working computer and one software license, as well as restrictions on the exchange of results between industry experts, will be raised.
digital imaging, segmentation, SIAMS, client-server technologies
1. Gonsales R., Vuds., Cifrovaya obrabotka izobrazheniy, 1104 s, 2012.
2. Hough P.V.C. Methods and Means for Recognizing Complex Patterns. U.S. Patent 3,069,654. 1964
3. Kadushnikov R.M., Kamenin I.G., Alievskiy V.M., Somina S.V., Chernyh S.E. Metod topologicheskoy rekonstrukcii i kolichestvennoy ocenki razmerov zeren. Zavodskaya laboratoriya,1997,4,s.30-34.
4. Kadushnikov P.M., Kamenin I.G., Alievskiy V.M., Chernyh S.E.,Somina S.V., Negashev B.C., Petrov M.S. Metod morfologicheskogo vosstanovleniya setki granic zeren. Cifrovaya mikroskopiya. Materialy shkolyseminara. Chast' 1. Ekaterinburg, 2001. s. 32-41.
5. Kadushnikov P.M., Grohovskiy V.I., Kamenin I.G., Alievskiy V.M., Chernyh S.E., Petrov M.S. Avtomatizirovannye metody analiza vklyucheniy grafita v chugune. Cifrovaya mikroskopiya. Materialy shkoly-seminara. Chast' 1. Ekaterinburg, 2001. s. 42-49.
6. Kadushnikov R.M. Grohovskiy V.I., Alievskiy V.M. Novye tehnologii razrabotki metodov analiza metallograficheskih izobrazheniy v cifrovoy mikroskopii. XVI Ural'skaya shkola metallovedov – termistov: «Problemy fizicheskogo metallovedeniya perspektivnyh materialov». Tezisy doklada. Ufa.
7. Petrov M. S., Kadushnikov R. M., Kamenin I. G. Alievskiy V. M., Alievskiy D. M., Negashev V. S., Nurkanov E. Yu. Metody matematicheskoy morfologii v zadachah analiza izobrazheniy. Cifrovaya mikroskopiya. Materialy shkoly-seminara. Chast' 1. Ekaterinburg, 2001. s. 60-64.
8. P. F. Felzenswalb, D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167-181p, 2004.
9. Payam S.Rahmdel, Richard Comley, Daming Shi and Siobhan McElduff. A Review of Hough Transform and Line Segment Detection Approaches. VISAPP 2015 – International Conferenceon Computer Vision Theory and Applications.
10. Prohorenok N. OpenCV i Java. Obrabotka izobrazheniy i komp'yuternoe zrenie. – SPb.: BHV-Peterburg, 2018. – 320 s.
11. Sivkova T.A., Gusev A.O., Gubarev S.V., Britsheva A.V., Samoylova A.Yu., Kadushnikov R.M. Osobennosti kontrolya mikrostruktury grafita v chugunah. Metallurgiya mashinostroeniya, 2, 2018g.
