The impact of the resolution of the measured object on the assessment of its perimeter

• Autorzy: Gądek-Moszczak Aneta

Identyfikatory

DOI

10.30657/pea.2019.25.09

• Języki publikacji: EN

Abstrakty

EN

Automatic detection of objects is a part of visual systems supporting a quality control system of a manufacturing process. The paper concerns the influence of the resolution of images and the size of detected objects in pixels on measurements results. Test images of the objects of a known size were generated. The values of the perimeter of the objects were compared to the obtained values of measurements on the images with degraded resolution. The process of the degradation of the references images by successive downsizing the resolution, detection and measurements were performed applying automatic algorithm. The analysis of obtained results showed that the size of the analysed objects on the digital images plays an important role in reliability and accuracy of the measurement. The author concludes that, in order to avoid a bias in measurement caused by insufficient object resolution, the minimal acceptable size of objects on digital images in pixels should be recommended.

Słowa kluczowe

EN

digital measurements; resolution; image processing; stereology

PL

pomiary cyfrowe; rozkład; przetwarzanie obrazu; stereologia; rozdzielczość obrazu

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2019
• Tom: Vol. 25
• Strony: 47--51
• Opis fizyczny: Bibliogr. 11 poz. rys., tab.

Twórcy

autor

Gądek-Moszczak Aneta

• Cracow University of Technology, Faculty of Mechanical Engineering, Institute of Applied Informatics, al. Jana Pawła II 37, 31-864 Kraków

Bibliografia

• 1. ASTM E2567 - 14:2014. Standard Test Method for Determining Nodularity And Nodule Count In Ductile Iron Using Image Analysis.
• 2. Batchelor, B.G, Whelan, P.F., 2012. Intelligent Vision Systems for Industry, Springer, London.
• 3. Gądek-Moszczak, A., Korzekwa, J., Fabiś-Domagała, J., 2019. The Impact of the image resolution on the value of measured geometric parameters on the example of ductile iron structure, Quality Production Improvement-QPI, 1, 1, 350-357.
• 4. Golnabi, H., Asadpour, A., 2007. Design and application of industrial machine vision systems, Robotics and Computer-Integrated Manufacturing, 23, 6, 630-637.
• 5. Gonzalez, R.C., Woods, R.E. 2008. Digital Image Processing, 3rd edition, Pearson Education.
• 6. Jia, H., Murphey, Y.L., Shi, J., Chang, T., 2004. An Intelligent Real-time Vision System for Surface Defect Detection. IEEE-Proceedings of the 17th International Conference on Pattern Recognition; 2004: 2-5.
• 7. Nirbhar Neog, Dusmanta K Mohanta, Pranab K Dutta, 2014. Review of vision-based steel surface inspection systems, EURASIP Journal on Image and Video Processing, 2014:50.
• 8. Russ, J.C., 1995. The Image Processing Handbook, CRC Press.
• 9. Russ, J.C., DeHoff, R.T., 2000. Practical stereology, Kluwer Academic/Plenum Publisher.
• 10. Shirvaikar, M., 2006. Trends in automated visual inspection, Real-Time Image Proccesing 1(1): 41-43.
• 11. Wojnar, L., Gądek, A. 2006. Ocena powtarzalności wyników ilościowej oceny struktury, Archiwum Odlewnictwa, 6, PAN, Katowice (in Polish).

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).