Research Article
BibTex RIS Cite

MACHINE WHELL EDGE DETECTION MORPHOLOGICAL OPERATIONS

Year 2024, Volume: 12 Issue: 1, 251 - 262, 01.03.2024
https://doi.org/10.36306/konjes.1418523

Abstract

One of the critical issues of image processing, defined as obtaining useful information from the image and improving the quality of the image, is edge detection. How edge detection performance will be affected by adding morphological operators to edge detection algorithms is among the issues that have not been fully resolved. In the study, Canny and Sobel edge detection algorithms were applied to different milling cutters used in machinability. Morphological operators were applied to the determined edges, and their effects on the edges were examined. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) values were used to compare the performances of edge detection algorithms. According to MSE and PSNR results, it was seen that the Canny algorithm gave better results than the Sobel algorithm. In addition, it was concluded that the images obtained as a result of the applied morphological operations provided better performance than the images that were not applied for both Canny and Sobel algorithms.

References

  • R. Choudhary, S. Gawade, ‘’Survey on Image Contrast Enhancement Techniques’’, International Journal of Innovative Studies in Sciences and Engineering Technology, vol. 2, is. 3, 2016.
  • C. R., Gonzalez, & R. E. Woods, ‘’Digital Image Processing’’. Pearson Education International, 2008.
  • D. Poobathy, & R. M. Chezian, ‘’Edge detection operators: Peak signal to noise ratio based comparison’’, IJ Image, Graphics and Signal Processing, 10, 55-61, 2014.
  • D. Poobathy and R. ManickaChezian, ‘’Recognizing and MiningVarious Objects from Digital Images’’, Proceedings of 2nd International Conference on Computer Applications and Information Technology (CAIT 2013), pp. 89-93, 2013.
  • C. Gentsos, C. Sotiropoulou, S. Nikolaidis, N. Vassiliadis, ‘’Real-time canny edge detection parallel implementation for FPGAs’’, IEEE international Conference On Electronics, Circuits and Systems, 2010.
  • P. Acharjya, R. Das, D. Ghoshal, ‘’Study and comparison of different edge detectors for image segmentation’’, Global Journal of Computer Science and Technology Graphics &Vision, 12 (13) (2012).
  • S. Kumar, M. Singh, D. Shaw, ‘’Comparative analysis of various edge detection techniques in biometric application’’, International Journal of Engineering and Technology (IJET), 8 (6) 2452-2459, 2016.
  • M. Hagar, P. Kubince, ‘’About edge detection in digital images’’, Radio engineering, 27 (4), 2018. 919-929.
  • Z. Stosic, , & P. Rutesic, ‘’An improved canny edge detection algorithm for detecting brain tumors in MRI images’’. International Journal of Signal Processing, 3, 2018.
  • Ravikumar, R., &Arulmozhi, V. ‘’Digital Image Processing-A QuickReview’’, International Journal of Intelligent Computing and Technology (IJICT), 2(2), 11-19, 2019.
  • S. S. Al-Amri, N. V.Kalyankar, , & S. D. Khamitkar, ‘’Image segmentation by using edge detection’’. International journal on computer science and engineering, 2(3), 804-807, 2010.
  • B. M. Chandra, D. Sonia, A. R. Devi, C. Y. Saraswathi,., K. M. Rathan, , & K. Bharghavi, ‘’Recognition of Vehicle NumberPlate Using Matlab’’, J. Univ. ShanghaiSci. Technol, 23(2), 363-370, 2021.
  • S. Rani, DeeptiBansal, and B. Kaur, “Detection of Edges Using Mathematical Morphological Operators,” Open Transactions On Information Processing, vol. 1, No. 1, pages 17- 26, may 2014.
  • https://www.nikon.com.tr/tr_TR/product/discontinued/digital-cameras/2015/d3100-black
  • F. Liu, et al. "Low computation and high efficiency Sobel edge detector for robot vision." 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2021.
  • U. Qidwai, and C.H. Chen, ‘’Digital image processing: an algorithmic approach with MATLAB’’, CRC Press, 2010.
  • F. S. Ning, & Y. C. Lee, ‘’Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan’’. Water, 13(19), 2774, 2021.
  • Moeslund T. ‘’Canny Edge detection’’. Denmark: Laboratory of Computer Vision and Media Technology, Aalborg University. 〈http://www.cvmt.dk/ education/teaching/f09/VGIS8/AIP/canny_09gr820.pdf〉, March 2009.
  • D. Rana, & S. Dalai, ‘’Review on traditional methods of edge detection to morphological based techniques’’, International Journal of Computer Science and Information Technologies, 5(4), 5915-5920, 2014.
  • S. Sridhar,”Digital Image Processing”,Oxford UniversityPress, 2012.
  • W. Kangtai & D. Wenzhan, ‘’Approach of Image Edge Detection Based on Sobel Operator and Grayrelation’’, Computer Applications, 26(5), 1035-1036, 2006.
  • R. Rana, & A. Verma, ‘’Comparison and Enhancement of Digital Image by Using Canny Filter and Sobel Filter’’. IOSR Journal of Computer Engineering, 16(1), 06-10, 2014.
  • H. Dong, ‘’Comparison of Edge Detection Techniques and Mathematical Morphology in Car Plate Detection Application’’. In 2021 7th International Conference on Computer and Communications (ICCC) (pp. 775-781). IEEE, 2021.
  • S. M. Abid Hasan, & K. Ko, ‘’Depth edge detection by image-based smoothing and morphological operations’’, Journal of Computational Design and Engineering, 3(3), 191-197, 2016.
  • N. G. Korkusuz, ‘’Otomatik hedef tanıma (ATR)’’, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, Elektronik ve Haberleşme Mühendisliği Anabilim Dalı, Yüksek Lisans Tezi, 2008.
  • K. C. Liao, H. Y. Wu, & H. T. Wen, ‘’Using Drones for Thermal Imaging Photography and Building 3D Images to Analyze the Defects of Solar Modules’’, Inventions, 7(3), 67, 2022.
  • O. Appiah, M. Asante, & J. B. Hayfron-Acquah, ‘’Improved approximated median filter algorithm for real-time computer vision applications’’. Journal of King Saud University-Computer and Information Sciences, 34(3), 782-792, 2022.
  • T. Chen, K. K. Ma and L. H. Chen, Tri-state median filter for image denoising. IEEE Transactions on Image processing, 8(12), 1834-1838, 1999.
  • İ. Kayadibi, G. E. Güraksin, & U. Ergün, ‘’ESA tabanlı göz durumu tespitinde görüntü önişlem yöntemlerinin etkisi’’, Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 1-1, 2022.
  • Jae S. Lim, ‘’Two-Dimensional Signal and Image Processing, Englewood Cliffs’’, NJ, Prentice Hall, pp. 478-488, 1990.
  • James R. Parker, ‘’Algorithms for Image Processing and Computer Vision’’, New York, John Wiley & Sons, Inc., pp. 23-29, 1997.
  • Z. Xu, X. Baojie, & W. Guoxin, ‘’ Canny edge detection based on Open CV’’. In 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI), pp. 53-56, 2017.
  • Y. Lu, L. Duanmu, Z. J. Zhai, & Z. Wang, ‘’Application and improvement of Canny edge-detection algorithm for exterior Wall hollowing detection using infrared thermal images’’. Energy and Buildings, 274, 112421. IEEE, 2022.
  • D. Dhillon,& R. Chouhan, ‘’Enhanced edge detection using SR-guided threshold maneuvering and window mapping: Handling broken edges and noisy structures in canny edges’’. IEEE Access, 10, 11191-11205, 2022.
  • W. Kong, J. Chen, Y. Song, Z. Fang, X. Yang, & H. Zhang, ‘’Sobel edge detection algorithm with adaptive threshold based on improved genetic algorithm for image processing’’, International Journal of Advanced Computer Science and Applications, 14(2),2023.
  • L. Berus, P. Skakun, D. Rajnovic, P. Janjatovic, L. Sidjanin, & M. Ficko, ‘’Determination of the grain size in single-phase materials by edge detection and concatenation’’, Metals, 10(10), 1381, 2020.
  • S. Vijayarani, & M. Vinupriya, ‘’Performance analysis of canny and sobel edge detection algorithms in image mining’’. International Journal of Innovative Research in Computer and Communication Engineering, 1(8), 1760-1767,2013.
  • Malbog, Mon Arjay F., et al. "Edge detection comparison of hybrid feature extraction for combustible fire segmentation: a Canny vs Sobel performance analysis." 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2020.
  • Remya Ajai, AS, and Gopalan Sundararaman. "Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images." Procedia Computer Science, 201, 487-494, 2022.
  • Q. Wu, & J. An, ‘’An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images’’. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3613-3626, 2013.
  • D. Vilimek, J. Kubicek, M. Golian, R. Jaros, R. Kahankova, P. Hanzlikova, & M.Buzga, ‘’Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images’’. Plos one, 17(7), 2022.
Year 2024, Volume: 12 Issue: 1, 251 - 262, 01.03.2024
https://doi.org/10.36306/konjes.1418523

Abstract

References

  • R. Choudhary, S. Gawade, ‘’Survey on Image Contrast Enhancement Techniques’’, International Journal of Innovative Studies in Sciences and Engineering Technology, vol. 2, is. 3, 2016.
  • C. R., Gonzalez, & R. E. Woods, ‘’Digital Image Processing’’. Pearson Education International, 2008.
  • D. Poobathy, & R. M. Chezian, ‘’Edge detection operators: Peak signal to noise ratio based comparison’’, IJ Image, Graphics and Signal Processing, 10, 55-61, 2014.
  • D. Poobathy and R. ManickaChezian, ‘’Recognizing and MiningVarious Objects from Digital Images’’, Proceedings of 2nd International Conference on Computer Applications and Information Technology (CAIT 2013), pp. 89-93, 2013.
  • C. Gentsos, C. Sotiropoulou, S. Nikolaidis, N. Vassiliadis, ‘’Real-time canny edge detection parallel implementation for FPGAs’’, IEEE international Conference On Electronics, Circuits and Systems, 2010.
  • P. Acharjya, R. Das, D. Ghoshal, ‘’Study and comparison of different edge detectors for image segmentation’’, Global Journal of Computer Science and Technology Graphics &Vision, 12 (13) (2012).
  • S. Kumar, M. Singh, D. Shaw, ‘’Comparative analysis of various edge detection techniques in biometric application’’, International Journal of Engineering and Technology (IJET), 8 (6) 2452-2459, 2016.
  • M. Hagar, P. Kubince, ‘’About edge detection in digital images’’, Radio engineering, 27 (4), 2018. 919-929.
  • Z. Stosic, , & P. Rutesic, ‘’An improved canny edge detection algorithm for detecting brain tumors in MRI images’’. International Journal of Signal Processing, 3, 2018.
  • Ravikumar, R., &Arulmozhi, V. ‘’Digital Image Processing-A QuickReview’’, International Journal of Intelligent Computing and Technology (IJICT), 2(2), 11-19, 2019.
  • S. S. Al-Amri, N. V.Kalyankar, , & S. D. Khamitkar, ‘’Image segmentation by using edge detection’’. International journal on computer science and engineering, 2(3), 804-807, 2010.
  • B. M. Chandra, D. Sonia, A. R. Devi, C. Y. Saraswathi,., K. M. Rathan, , & K. Bharghavi, ‘’Recognition of Vehicle NumberPlate Using Matlab’’, J. Univ. ShanghaiSci. Technol, 23(2), 363-370, 2021.
  • S. Rani, DeeptiBansal, and B. Kaur, “Detection of Edges Using Mathematical Morphological Operators,” Open Transactions On Information Processing, vol. 1, No. 1, pages 17- 26, may 2014.
  • https://www.nikon.com.tr/tr_TR/product/discontinued/digital-cameras/2015/d3100-black
  • F. Liu, et al. "Low computation and high efficiency Sobel edge detector for robot vision." 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2021.
  • U. Qidwai, and C.H. Chen, ‘’Digital image processing: an algorithmic approach with MATLAB’’, CRC Press, 2010.
  • F. S. Ning, & Y. C. Lee, ‘’Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan’’. Water, 13(19), 2774, 2021.
  • Moeslund T. ‘’Canny Edge detection’’. Denmark: Laboratory of Computer Vision and Media Technology, Aalborg University. 〈http://www.cvmt.dk/ education/teaching/f09/VGIS8/AIP/canny_09gr820.pdf〉, March 2009.
  • D. Rana, & S. Dalai, ‘’Review on traditional methods of edge detection to morphological based techniques’’, International Journal of Computer Science and Information Technologies, 5(4), 5915-5920, 2014.
  • S. Sridhar,”Digital Image Processing”,Oxford UniversityPress, 2012.
  • W. Kangtai & D. Wenzhan, ‘’Approach of Image Edge Detection Based on Sobel Operator and Grayrelation’’, Computer Applications, 26(5), 1035-1036, 2006.
  • R. Rana, & A. Verma, ‘’Comparison and Enhancement of Digital Image by Using Canny Filter and Sobel Filter’’. IOSR Journal of Computer Engineering, 16(1), 06-10, 2014.
  • H. Dong, ‘’Comparison of Edge Detection Techniques and Mathematical Morphology in Car Plate Detection Application’’. In 2021 7th International Conference on Computer and Communications (ICCC) (pp. 775-781). IEEE, 2021.
  • S. M. Abid Hasan, & K. Ko, ‘’Depth edge detection by image-based smoothing and morphological operations’’, Journal of Computational Design and Engineering, 3(3), 191-197, 2016.
  • N. G. Korkusuz, ‘’Otomatik hedef tanıma (ATR)’’, Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, Elektronik ve Haberleşme Mühendisliği Anabilim Dalı, Yüksek Lisans Tezi, 2008.
  • K. C. Liao, H. Y. Wu, & H. T. Wen, ‘’Using Drones for Thermal Imaging Photography and Building 3D Images to Analyze the Defects of Solar Modules’’, Inventions, 7(3), 67, 2022.
  • O. Appiah, M. Asante, & J. B. Hayfron-Acquah, ‘’Improved approximated median filter algorithm for real-time computer vision applications’’. Journal of King Saud University-Computer and Information Sciences, 34(3), 782-792, 2022.
  • T. Chen, K. K. Ma and L. H. Chen, Tri-state median filter for image denoising. IEEE Transactions on Image processing, 8(12), 1834-1838, 1999.
  • İ. Kayadibi, G. E. Güraksin, & U. Ergün, ‘’ESA tabanlı göz durumu tespitinde görüntü önişlem yöntemlerinin etkisi’’, Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 1-1, 2022.
  • Jae S. Lim, ‘’Two-Dimensional Signal and Image Processing, Englewood Cliffs’’, NJ, Prentice Hall, pp. 478-488, 1990.
  • James R. Parker, ‘’Algorithms for Image Processing and Computer Vision’’, New York, John Wiley & Sons, Inc., pp. 23-29, 1997.
  • Z. Xu, X. Baojie, & W. Guoxin, ‘’ Canny edge detection based on Open CV’’. In 2017 13th IEEE international conference on electronic measurement & instruments (ICEMI), pp. 53-56, 2017.
  • Y. Lu, L. Duanmu, Z. J. Zhai, & Z. Wang, ‘’Application and improvement of Canny edge-detection algorithm for exterior Wall hollowing detection using infrared thermal images’’. Energy and Buildings, 274, 112421. IEEE, 2022.
  • D. Dhillon,& R. Chouhan, ‘’Enhanced edge detection using SR-guided threshold maneuvering and window mapping: Handling broken edges and noisy structures in canny edges’’. IEEE Access, 10, 11191-11205, 2022.
  • W. Kong, J. Chen, Y. Song, Z. Fang, X. Yang, & H. Zhang, ‘’Sobel edge detection algorithm with adaptive threshold based on improved genetic algorithm for image processing’’, International Journal of Advanced Computer Science and Applications, 14(2),2023.
  • L. Berus, P. Skakun, D. Rajnovic, P. Janjatovic, L. Sidjanin, & M. Ficko, ‘’Determination of the grain size in single-phase materials by edge detection and concatenation’’, Metals, 10(10), 1381, 2020.
  • S. Vijayarani, & M. Vinupriya, ‘’Performance analysis of canny and sobel edge detection algorithms in image mining’’. International Journal of Innovative Research in Computer and Communication Engineering, 1(8), 1760-1767,2013.
  • Malbog, Mon Arjay F., et al. "Edge detection comparison of hybrid feature extraction for combustible fire segmentation: a Canny vs Sobel performance analysis." 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2020.
  • Remya Ajai, AS, and Gopalan Sundararaman. "Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images." Procedia Computer Science, 201, 487-494, 2022.
  • Q. Wu, & J. An, ‘’An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images’’. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3613-3626, 2013.
  • D. Vilimek, J. Kubicek, M. Golian, R. Jaros, R. Kahankova, P. Hanzlikova, & M.Buzga, ‘’Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images’’. Plos one, 17(7), 2022.
There are 41 citations in total.

Details

Primary Language English
Subjects Photogrametry
Journal Section Research Article
Authors

Pınar Karakuş 0000-0003-3727-7233

Publication Date March 1, 2024
Submission Date January 12, 2024
Acceptance Date February 9, 2024
Published in Issue Year 2024 Volume: 12 Issue: 1

Cite

IEEE P. Karakuş, “MACHINE WHELL EDGE DETECTION MORPHOLOGICAL OPERATIONS”, KONJES, vol. 12, no. 1, pp. 251–262, 2024, doi: 10.36306/konjes.1418523.