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Article

Neutrosophic Hough Transform

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Department of Electrical-Electronics Engineering, Engineering Faculty, Bitlis Eren University, 13000 Bitlis, Turkey
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Department of Computer Science, University of Illinois at Springfield, One University Plaza, Springfield, IL 62703, USA
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Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
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Mathematics & Science Department, University of New Mexico, 705 Gurley Ave., Gallup, NM 87301, USA
*
Author to whom correspondence should be addressed.
Axioms 2017, 6(4), 35; https://doi.org/10.3390/axioms6040035
Received: 22 November 2017 / Revised: 13 December 2017 / Accepted: 14 December 2017 / Published: 18 December 2017
(This article belongs to the Special Issue Neutrosophic Multi-Criteria Decision Making)
Hough transform (HT) is a useful tool for both pattern recognition and image processing communities. In the view of pattern recognition, it can extract unique features for description of various shapes, such as lines, circles, ellipses, and etc. In the view of image processing, a dozen of applications can be handled with HT, such as lane detection for autonomous cars, blood cell detection in microscope images, and so on. As HT is a straight forward shape detector in a given image, its shape detection ability is low in noisy images. To alleviate its weakness on noisy images and improve its shape detection performance, in this paper, we proposed neutrosophic Hough transform (NHT). As it was proved earlier, neutrosophy theory based image processing applications were successful in noisy environments. To this end, the Hough space is initially transferred into the NS domain by calculating the NS membership triples (T, I, and F). An indeterminacy filtering is constructed where the neighborhood information is used in order to remove the indeterminacy in the spatial neighborhood of neutrosophic Hough space. The potential peaks are detected based on thresholding on the neutrosophic Hough space, and these peak locations are then used to detect the lines in the image domain. Extensive experiments on noisy and noise-free images are performed in order to show the efficiency of the proposed NHT algorithm. We also compared our proposed NHT with traditional HT and fuzzy HT methods on variety of images. The obtained results showed the efficiency of the proposed NHT on noisy images. View Full-Text
Keywords: Hough transform; fuzzy Hough transform; neutrosophy theory; line detection Hough transform; fuzzy Hough transform; neutrosophy theory; line detection
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MDPI and ACS Style

Budak, Ü.; Guo, Y.; Şengür, A.; Smarandache, F. Neutrosophic Hough Transform. Axioms 2017, 6, 35. https://doi.org/10.3390/axioms6040035

AMA Style

Budak Ü, Guo Y, Şengür A, Smarandache F. Neutrosophic Hough Transform. Axioms. 2017; 6(4):35. https://doi.org/10.3390/axioms6040035

Chicago/Turabian Style

Budak, Ümit, Yanhui Guo, Abdulkadir Şengür, and Florentin Smarandache. 2017. "Neutrosophic Hough Transform" Axioms 6, no. 4: 35. https://doi.org/10.3390/axioms6040035

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