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Yıl 2020, Cilt: 4 Sayı: 3, 132 - 137, 30.09.2020

Murat Alparslan Güngör Seyfi Polat

https://doi.org/10.30939/ijastech..726332
Cited By: 1

Öz

Kaynakça

  • [1] Sobel, I. (1970). Camera Models and Perception. Ph.D. the-sis, Stanford University, CA.
  • [2] Prewitt, J. (1970). Object Enhancemet and Extraction. Pic-ture Processing and Psychopictorics, NY, Academic Pres.
  • [3] Canny, J. (1986). A Computational Approach to Edge Detec-tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679-700.
  • [4] Jin, S., Li, X., Yang, X., Zhang, J. A., and Shen, D. (2019). Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimiza-tion Algorithms. IEEE Transactions on Geoscience and Re-mote Sensing, 57(1), 598-608.
  • [5] Rafati, M., Arabfard, M., Zadeh, M. R. R., and Maghsoud-loo, M. (2016). Assessment of noise reduction in ultrasound images of common carotid and brachial arteries. IET Com-puter Vision, 10(1), 1-8.
  • [6] Kalra, A., and Chhokar, R. L. (2016, September). A Hybrid approach using sobel and canny operator for digital image edge detection. In: 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 305-310.
  • [7] Guiming, S., and Jidong, S. (2016, June). Remote sensing image edge-detection based on improved Canny operator. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 652-656.
  • [8] Ye, H., Shen, B., and Yan, S. (2018, October). Prewitt edge detection based on BM3D image denoising. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Au-tomation Control Conference (IAEAC), pp. 1593-1597.
  • [9] Attivissimo, F., Cavone, G., Lanzolla, A. M. L., and Spa-davecchia, M. (2010). A technique to improve the image quality in computer tomography. IEEE Transactions on In-strumentation and Measurement, 59(5), 1251-1257.
  • [10] Furnari, A., Farinella, G. M., Bruna, A. R., and Battiato, S. (2017). Distortion adaptive Sobel filters for the gradient es-timation of wide angle images. Journal of Visual Communi-cation and Image Representation, 46, 165-175.
  • [11] Menaka, R., Janarthanan, S., and Deeba, K. (2020). FPGA Implementation of Low Power and High Speed Image Edge Detection Algorithm. Microprocessors and Microsystems, 103053.
  • [12] Biswas, S., and Ghoshal, D. (2016). Blood cell detection using thresholding estimation based watershed transfor-mation with Sobel filter in frequency domain. Procedia Computer Science, 89, 651-657.
  • [13] Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., and Ramirez-Gonzalez, G. (2018). Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cognitive Systems Research, 50, 10-14.
  • [14] Aybar, E. (2008). Edge Detection On Color Images Using Sobel Operator. Afyon Kocatepe University Journal of Sci-ence, 8(1), 205-217.
  • [15] Aybar, E. (2014). Edge Detection Using Connection MAP. Anadolu University Journal of Science and Technology B - Theoritical Sciences (AUJST-B), 3(1), 21-32.
  • [16] Lalimi, M. A., Ghofrani, S., and McLernon, D. (2013). A vehicle license plate detection method using region and edge based methods. Computers & Electrical Engineering, 39(3), 834-845.
  • [17] Hsia, C. H., Wu, T. C., and Chiang, J. S. (2017). A new method of moving object detection using adaptive filter. Journal of Real-Time Image Processing, 13(2), 311-325.
  • [18] Szczepanski, M. (2019). Fast spatio-temporal digital paths video filter. Journal of Real-Time Image Processing,16(2), 477-489.
  • [19] Ahmad, N. S., Zaki, Z. M., and Ismail, W. (2014, Septem-ber). Region of adaptive threshold segmentation between mean, median and otsu threshold for dental age assessment. In: 2014 International Conference on Computer, Communi-cations, and Control Technology (I4CT), pp. 353-356.
  • [20] Rongwen Lu (2020). basic global threshold-ing (https://www.mathworks.com/matlabcentral/fileexchange/38390-basic-global-thresholding), MATLAB Central File Exchange. Retrieved April 3, 2020.

Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances

Yıl 2020, Cilt: 4 Sayı: 3, 132 - 137, 30.09.2020

Murat Alparslan Güngör Seyfi Polat

https://doi.org/10.30939/ijastech..726332
Cited By: 1

Öz

Determining the borders of the objects in the images and detection of the edges is an important step for image processing applications. In this study, the areas of different objects were calculated using edge detection methods. The edges of real-time images taken from the camera were detected with traditionally used Sobel, Prewitt and Canny filters. In addition, these filters were applied to the images obtained using Basic Global Thresholding (BGT) pre-processing and Mean Thresholding (MT) pre-processing to detect the edges in the image. After eliminating the fake edges in the edge images, the areas of the obtained images were calculated. The methods used in this paper were compared according to the fake edge and the area calculation performances.

Anahtar Kelimeler

Area calculation Edge detection Fake edge Thresholding

Kaynakça

  • [1] Sobel, I. (1970). Camera Models and Perception. Ph.D. the-sis, Stanford University, CA.
  • [2] Prewitt, J. (1970). Object Enhancemet and Extraction. Pic-ture Processing and Psychopictorics, NY, Academic Pres.
  • [3] Canny, J. (1986). A Computational Approach to Edge Detec-tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679-700.
  • [4] Jin, S., Li, X., Yang, X., Zhang, J. A., and Shen, D. (2019). Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimiza-tion Algorithms. IEEE Transactions on Geoscience and Re-mote Sensing, 57(1), 598-608.
  • [5] Rafati, M., Arabfard, M., Zadeh, M. R. R., and Maghsoud-loo, M. (2016). Assessment of noise reduction in ultrasound images of common carotid and brachial arteries. IET Com-puter Vision, 10(1), 1-8.
  • [6] Kalra, A., and Chhokar, R. L. (2016, September). A Hybrid approach using sobel and canny operator for digital image edge detection. In: 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 305-310.
  • [7] Guiming, S., and Jidong, S. (2016, June). Remote sensing image edge-detection based on improved Canny operator. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 652-656.
  • [8] Ye, H., Shen, B., and Yan, S. (2018, October). Prewitt edge detection based on BM3D image denoising. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Au-tomation Control Conference (IAEAC), pp. 1593-1597.
  • [9] Attivissimo, F., Cavone, G., Lanzolla, A. M. L., and Spa-davecchia, M. (2010). A technique to improve the image quality in computer tomography. IEEE Transactions on In-strumentation and Measurement, 59(5), 1251-1257.
  • [10] Furnari, A., Farinella, G. M., Bruna, A. R., and Battiato, S. (2017). Distortion adaptive Sobel filters for the gradient es-timation of wide angle images. Journal of Visual Communi-cation and Image Representation, 46, 165-175.
  • [11] Menaka, R., Janarthanan, S., and Deeba, K. (2020). FPGA Implementation of Low Power and High Speed Image Edge Detection Algorithm. Microprocessors and Microsystems, 103053.
  • [12] Biswas, S., and Ghoshal, D. (2016). Blood cell detection using thresholding estimation based watershed transfor-mation with Sobel filter in frequency domain. Procedia Computer Science, 89, 651-657.
  • [13] Vardhana, M., Arunkumar, N., Lasrado, S., Abdulhay, E., and Ramirez-Gonzalez, G. (2018). Convolutional neural network for bio-medical image segmentation with hardware acceleration. Cognitive Systems Research, 50, 10-14.
  • [14] Aybar, E. (2008). Edge Detection On Color Images Using Sobel Operator. Afyon Kocatepe University Journal of Sci-ence, 8(1), 205-217.
  • [15] Aybar, E. (2014). Edge Detection Using Connection MAP. Anadolu University Journal of Science and Technology B - Theoritical Sciences (AUJST-B), 3(1), 21-32.
  • [16] Lalimi, M. A., Ghofrani, S., and McLernon, D. (2013). A vehicle license plate detection method using region and edge based methods. Computers & Electrical Engineering, 39(3), 834-845.
  • [17] Hsia, C. H., Wu, T. C., and Chiang, J. S. (2017). A new method of moving object detection using adaptive filter. Journal of Real-Time Image Processing, 13(2), 311-325.
  • [18] Szczepanski, M. (2019). Fast spatio-temporal digital paths video filter. Journal of Real-Time Image Processing,16(2), 477-489.
  • [19] Ahmad, N. S., Zaki, Z. M., and Ismail, W. (2014, Septem-ber). Region of adaptive threshold segmentation between mean, median and otsu threshold for dental age assessment. In: 2014 International Conference on Computer, Communi-cations, and Control Technology (I4CT), pp. 353-356.
  • [20] Rongwen Lu (2020). basic global threshold-ing (https://www.mathworks.com/matlabcentral/fileexchange/38390-basic-global-thresholding), MATLAB Central File Exchange. Retrieved April 3, 2020.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Research Articles
Yazarlar

Murat Alparslan Güngör 0000-0001-7446-7808

Seyfi Polat 0000-0002-7196-3053

Yayımlanma Tarihi 30 Eylül 2020
Gönderilme Tarihi 24 Nisan 2020
Kabul Tarihi 10 Temmuz 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 4 Sayı: 3

Kaynak Göster

APA Güngör, M. A., & Polat, S. (2020). Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances. International Journal of Automotive Science And Technology, 4(3), 132-137. https://doi.org/10.30939/ijastech..726332
AMA Güngör MA, Polat S. Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances. ijastech. Eylül 2020;4(3):132-137. doi:10.30939/ijastech.726332
Chicago Güngör, Murat Alparslan, ve Seyfi Polat. “Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances”. International Journal of Automotive Science And Technology 4, sy. 3 (Eylül 2020): 132-37. https://doi.org/10.30939/ijastech. 726332.
EndNote Güngör MA, Polat S (01 Eylül 2020) Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances. International Journal of Automotive Science And Technology 4 3 132–137.
IEEE M. A. Güngör ve S. Polat, “Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances”, ijastech, c. 4, sy. 3, ss. 132–137, 2020, doi: 10.30939/ijastech..726332.
ISNAD Güngör, Murat Alparslan - Polat, Seyfi. “Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances”. International Journal of Automotive Science And Technology 4/3 (Eylül 2020), 132-137. https://doi.org/10.30939/ijastech. 726332.
JAMA Güngör MA, Polat S. Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances. ijastech. 2020;4:132–137.
MLA Güngör, Murat Alparslan ve Seyfi Polat. “Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances”. International Journal of Automotive Science And Technology, c. 4, sy. 3, 2020, ss. 132-7, doi:10.30939/ijastech. 726332.
Vancouver Güngör MA, Polat S. Comparison of the Edge Detection Methods According to Fake Edge and Area Calculation Performances. ijastech. 2020;4(3):132-7.

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International Journal of Automotive Science and Technology (IJASTECH) is published by Society of Automotive Engineers Turkey

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