Plenary Speakers

Prof. Mariusz Flasiński

Jagiellonian University, Paradigmatic Considerations on Syntactic Pattern Recognition

Graph grammar models in syntactic pattern recognition

Prof. Krzysztof Okarma

West Pomeranian University of Technology, Szczecin, Poland

Computer vision methods for non-destructive quality assessment in additive manufacturing

Increasing availability and popularity of 3D printers cause growing interest in monitoring of additive manufacturing processes as well as quality assessment and classification of 3D printed objects. For this purpose various methods can be used, in some cases dependent on the type of filament, including X-ray tomography and ultrasonic imaging as well as electromagnetic methods e.g. terahertz non-destructive testing. Nevertheless, in many typical low cost solutions, utilising Fused Deposition Modelling (FDM) based technology, the practical application of such methods can be troublesome. Therefore, on-line quality assessment of the 3D printed surfaces using image analysis methods seems to be a good alternative, allowing to detect the quality decrease and stop the printing process or correct the surface in case of minor distortions to save time, energy and material. From aesthetic point of view the quality assessment results may be correlated with human perception of surface’s quality, whereas, considering the physical issues, the presence of some surface distortions may indicate poor mechanical properties of the 3D printed object.The challenging problem of a reliable quality assessment of the 3D printed surfaces and appropriate classification of the printed samples can be solved using various computer vision approaches. Interesting results can be obtained assuming the appropriate location of the camera and analysing the side view of the printed object where the linear patterns representing consecutive layers of the filament can be easily observed, especially for flat surfaces. Some exemplary experimental results of the application of texture analysis with the use of GLCM and Haralick features, Hough transform, similarity based image quality metrics, Fourier analysis and entropy are presented.

Prof. Piotr Porwik

University of Silesia, Katowice, Poland

Machine learning approach in biometrics and medicine

Machine learning is a subject that studies programming computer methods to simulate human learning activities. So, machine learning algorithms focus on how computers learn from complete, incomplete and unbalanced data. Realized objects recognition by human brain is very complicated biological process, and it can be imitated by artificial intelligence. In machine learning, statistical models, mathematics as well as pattern analysis and image preprocessing are widely connected and this marriage allows to solving some of recognition tasks. In biometrics, human personal traits, mainly for security and authentication purposes, allows helping in identification or verification of persons. Nowadays, it is intensively developed domain supported by international agencies and governments many countries. Biometric data are for many years known and can be relatively easily enrolment. It should be emphasized that now, there are state-of-the-art techniques based on machine learning algorithms which allows to achieve very high recognition level of individuals. These approaches will be presented during lecture. Similar machine learning methods can be used in the medical field. The purpose of this lecture will be to show what medicine procedures might benefit from learning approaches. It is important to note that large medical data sets are available for many decades – and yet, papers applying machine learning algorithms, relatively few have contributed meaningfully to clinical practice. Some practical realization will be discussed.

Contact


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Wroclaw University of Technology
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