PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | 24 | Special Issue S3 |
Tytuł artykułu

A comparative study on the method of extracting edge and contour information of multifunctional digital ship image

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The result of the extraction of the edge and contour information of the multifunctional digital ship image directly affects the evaluation and recognition of the subsequent image quality. At present, the common method used to extract the edge contour information is based on the Canny operator, and there is a problem that the edge is not clear.In order to obtain more accurate edge information, a method of extracting edge and contour information of multimedia digital image based on multi-scale morphology is proposed. Firstly, the digital ship image is made double filter and the fuzzy threshold segmentation, and then the edge and contour information is extracted by multi-scale morphology. Experiments show that the proposed method can obtain more accurate edge information compared with the other methods
Słowa kluczowe
EN
Wydawca
-
Rocznik
Tom
24
Opis fizyczny
p.228-234,fig.,ref.
Twórcy
autor
  • Guangdong Mechanical and Electrical College, Guangzhou 510515, China
Bibliografia
  • 1. Tandon R, Simeone O. Harnessing cloud and edge synergies: toward an information theory of fog radio access networks . IEEE Communications Magazine, 2016, 54(8):44-50.
  • 2. Kuo P C, Lu K H, Hsu Y N, et al. Fast three-dimensional video coding encoding algorithms based on edge information of depth map. Iet Image Processing, 2015, 9(7):587-595.
  • 3. Cheng B N, Kuperman G, Deutsch P, et al. Group-centric networking: addressing information sharing requirements at the tactical edge. IEEE Communications Magazine, 2016, 54(10):145-151.
  • 4. Wen S, Haghighi M S, Chen C, et al. A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks. IEEE Transactions on Computers, 2015, 64(3):640-653.
  • 5. Liu X F, Yao X R, Lan R M, et al. Edge detection based on gradient ghost imaging. Optics Express, 2015, 23(26):33802.
  • 6. Tseng C S, Wang J H. Perceptual edge detection via entropydriven gradient evaluation. Iet Computer Vision, 2016, 10(2):163-171.
  • 7. Hidalgogato M C, Barbosa V C F. Edge detection of potential-field sources using scale-space monogenic signal: Fundamental principles. Geophysics, 2015, 80(5):J27–J36.
  • 8. Hidalgogato M C, Barbosa V C F. Edge detection of potential-field sources using scale-space monogenic signal: Fundamental principles. Geophysics, 2015, 80(5):J27–J36.
  • 9. Liu X, Fang S. A convenient and robust edge detection method based on ant colony optimization. Optics Communications, 2015, 353(8):147-157.
  • 10. Gardiner B, Coleman S A, Scotney B W. Multiscale Edge Detection Using a Finite Element Framework for Hexagonal Pixel-Based Images. IEEE Transactions on Image Processing, 2016, 25(4):1-1.
  • 11. Zheng Y, Zhou Y, Zhou H, et al. Ultrasound image edge detection based on a novel multiplicative gradient and Canny operator.. Ultrasonic Imaging, 2015, 37(3):238-50.
  • 12. Sharma B, Mahajan P. Latest trend of variation of EDGE detection and object detection with pixel level variation and their comparison algorithms. Genetic Epidemiology, 2015, 35(7):606-19.
  • 13. Tschirhart P, Morris B. Improved edge detection mapping through stacking and integration: a case study in the Bathurst Mining Camp. Geophysical Prospecting, 2015, 63(2):283-295.
  • 14. Qu Z, Fang X, Su H, et al. Measurements for displacement and deformation at high temperature by using edge detection of digital image.. Applied Optics, 2015, 54(29):8731.
  • 15. 11.Gao, W. and W. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones. Journal of Difference Equations and Applications, 2017. 23(1-2SI): p. 100-109.
  • 16. 12.Gao, W., et al., Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Computing-The Journal of Networks Software Tools and Applications, 2017. 20(2SI): p. 959-968.
  • 17. Any support wen-quan zeng, ai-min yu. An effective medical noisy image edge detection method . Journal of electronic design engineering, 2016, 24 (10) : 180-183.
  • 18. Rahman N A, HalimH, Gotoh H, Harada E. Validation of Microscopic Dynamics of Grouping Pedestrians Behavior: From Observation to Modeling and Simulation. Engineering Heritage Journal, 2017, 1(2):15–18.
  • 19. Gao W,Rajesh Kanna M R, Suresh E, Farahani M R. Calculating of degree-based topological indices of nanostructures. Geology, Ecology, and Landscapes, 2017, 1(3): 173-183.
  • 20. Farajollahi G, Delavar M R. Assessing accident hotspots by using volunteered geographic information. Journal CleanWAS, 2017, 1(2): 14-17.
  • 21. Roslee R,Mickey A C, Simon N, Norhisham M N. Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay Method (Wom) Along the Genting Sempah To Bentong Highway, Pahang. Malaysian Journal Geosciences, 2017, 1(2): 13-19.
  • 22. Ismail M N, Rahman A, Tahir S H. Wave-dominated shoreline deposits in the Late Miocene Sedimentary Sequence in the Miri Formation North Sarawak, Malaysia. Geological Behavior, 2017, 1(2):14–19.
  • 23. TengY, Zhou Q. Environmental effect of Sudan I-IV: adsorption behaviors and potential risk on soil. Acta Scientifica Malaysia, 2017, 1(1): 16-17.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.agro-45cec4df-d22f-4164-bb9b-4be7559196bc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.