PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

Review of edge detection algorithms for application in miniature dimension measurement modules

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Continuous improvement of semiconductor chip technology allows more and more complex functions to be performed by an increasing number of modules with limited space and power. The deep miniaturisation and cost reduction of such modules has a positive impact on their application areas. They can be used, for example, inside machine tools for dimensional inspection of workpieces or deformation of the tool tip. Modules of this type must contain as many standard components as possible and, in particular, a processing unit responsible, among other things, for processing the optical sensor signal. For this reason, this study reviews and compares algorithms for object edge detection useful in embedded systems based on standard single chips, cores based on Cortex-M4F. The complexity of processing and the quality of algorithm output data was analysed. The results of experimental studies are presented. It was found that the required processing times significantly reduce the use of single chip embedded systems only for edge detection on small images.
Rocznik
Strony
74--85
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
  • Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland
  • Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland
Bibliografia
  • [1] HASKAH J., PIES M., MACHACEK Z., 2014, Image Signal Processing, Analysis and Detection for Robotic System, 14th International Conference on Control, Automation and Systems (ICC AS 2014) Oct. 22–25, 2014 in KINTEX, Oyeonggi-do, Korea.
  • [2] RULANINGTYAS R., AIN K., 2009, Edge Detection for Brain Tumor Pattern Recognition. Instrumentation, Communications, Information Technology and Biomedical Engineering(ICICI-BME), International Conferenceon; Nov 23–25; Bandung, Indonesia. IEEE. 1–3.
  • [3] LIN H., DU P.J., ZHAO C.S., SHU N., 2004, Edge Detection Method of Remote Sensing Images Based on Mathematical Morphology of Multi-Structure Elements, Chinese Geographical Science, 14/3, 263–268.
  • [4] YANG W., 2019, Analysis of Sports Image Detection Technology Based on Machine Learning, EURASIP Journal on Image and Video Processing, 17.
  • [5] WANG K., 2020, Tool Thermal Deformation Measurement Research Based on Image Processing Technology, Proceedings 3rd International Conference on Electron Device and Mechanical Engineering, ICEDME, May, 714–717.
  • [6] ZHICHAO Y., 2020, On-Line Milling Cutter Wear Monitoring in a Wide Field-of-View Camera, Wear, 460–461, 203479.
  • [7] DUAN L., YAO M., ZHANG H., 2019, The Research and Design of a High Efficient Ball Image Recognition System Based on Microcontroller, IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC.
  • [8] SATO T., KRUPA G., ZOCCAL L., Performance Measurements of Sobel Edge Detection Implemented in an FPGA Architecture and Software, Proceedings of the 4th Brazilian Technology Symposium (BTSym’18), Smart Innovation, Systems and Technologies, 140.
  • [9] CANNY J., 1986, A Computational Approach to Edge Detection. Pattern Analysis and Machine Intelligence, IEEE, 8/6 679–698.
  • [10] ZHANG J., CHEN Y., HUANG X., 2009, Edge Detection of Images Based on Improved Sobel Operator and Genetic Algorithms, Image Analysis and Signal Processing, IASP International, April 11–12; Taizhou, China. IEEE, 31–35.
  • [11] ROSENFELD A., 1981, The Max Roberts Operator is a Hueckel-Type Edge Detector. Pattern Analysis and Machine Intelligence, IEEE, 3/1 101–103.
  • [12] LEI Y., DEWEI Z., XIAOYU W., HUI L., JUN Z., 2011, An Improved Prewitt Algorithm for Edge Detection Based on Noised Image, Image and Signal Processing (CISP), 4th International Congress on, Oct. 15–17; Shanghai, China. IEEE, 1197–1200.
  • [13] Ulupinar F., Medioni G., 1990, Refining edges detected by a LoG operator, Computer Vision, Graphics and Image Processing, 51/3, 275–298.
  • [14] TORTE V., POGGIO T.A., 1986, On Edge Detection, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 147–163.
  • [15] CHEN M.H., LEE D., PAVLIDIS T., 1991, Residual Analysis for Feature Detection, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-13, 30–40.
  • [16] ZADEH., L.A., 1984, Fuzzy Probabilities, Information Processing & Management, 20/3, 363–372.
  • [17] PERONA P., MALIK J., 1990, Scale Space and Edge Detection Using Anisotropic Diffusion, IEEE Trans. Pattern Anal. Mach. Intell. PAMi-12, 6296–39.
  • [18] SHEN J., CASTAN S., 1992, An Optimal Linear Operator for Step Edge Detection, CVGIP: Graph. Models Image Process, 54, 112–133.
  • [19] PREWITT, J.M.S., 1970, Object Enhancement and Extraction, Picture processing and Psychopictorics. Academic Press.
  • [20] MARR D., HILDRETH E., 1980, Theory of Edge Detection, Proceedings of the Royal Society of London. Series B, Biological Sciences, 207 (1167), 187–217
  • [21] HAQ I., ANWAR S., SHAH K., KHAN M.T., SHAH S.A., 2015, Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images, PLoS ONE 10/9, e0138712.
  • [22] BHANDARKAR S.M., ZHANG Y., Potter W.D., 1994, An Edge Detection Technique Using Genetic Algorithm Based Optimization, Pattern Recognition, 27/9, 1159–1180.
  • [23] KIRKPATRICK S., GELATT C.D.Jr, VECCHI M.P.Jr, 1983, Optimization by Simulated Annealing, Science, 220, 671–680.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-53f74dda-088d-4eff-ba2f-6375949f1e48
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.