A Novel Edge Detection Algorithm Based on Texture Feature Coding
A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2015-06-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2014-0075 |
| _version_ | 1831560162403418112 |
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| author | Sengur Abdulkadir Guo Yanhui Ustundag Mehmet Alcin Ömer Faruk |
| author_facet | Sengur Abdulkadir Guo Yanhui Ustundag Mehmet Alcin Ömer Faruk |
| author_sort | Sengur Abdulkadir |
| collection | DOAJ |
| description | A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector. |
| first_indexed | 2024-12-17T05:40:20Z |
| format | Article |
| id | doaj.art-bae40409734e4c8e9eb5f22568d1a4a4 |
| institution | Directory Open Access Journal |
| issn | 0334-1860 2191-026X |
| language | English |
| last_indexed | 2024-12-17T05:40:20Z |
| publishDate | 2015-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-bae40409734e4c8e9eb5f22568d1a4a42022-12-21T22:01:27ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2015-06-0124223524810.1515/jisys-2014-0075A Novel Edge Detection Algorithm Based on Texture Feature CodingSengur Abdulkadir0Guo Yanhui1Ustundag Mehmet2Alcin Ömer Faruk3Department of Electric and Electronics Engineering, Firat University, Elazig, TurkeySchool of Science, Technology and Engineering Management, St. Thomas University, Miami Gardens, FL 33054, USADepartment of Electronics and Computer Science, Firat University, Elazig, TurkeyDepartment of Electronics and Computer Science, Firat University, Elazig, TurkeyA new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector.https://doi.org/10.1515/jisys-2014-0075edge detectiontexture feature codingimage segmentationfpga |
| spellingShingle | Sengur Abdulkadir Guo Yanhui Ustundag Mehmet Alcin Ömer Faruk A Novel Edge Detection Algorithm Based on Texture Feature Coding Journal of Intelligent Systems edge detection texture feature coding image segmentation fpga |
| title | A Novel Edge Detection Algorithm Based on Texture Feature Coding |
| title_full | A Novel Edge Detection Algorithm Based on Texture Feature Coding |
| title_fullStr | A Novel Edge Detection Algorithm Based on Texture Feature Coding |
| title_full_unstemmed | A Novel Edge Detection Algorithm Based on Texture Feature Coding |
| title_short | A Novel Edge Detection Algorithm Based on Texture Feature Coding |
| title_sort | novel edge detection algorithm based on texture feature coding |
| topic | edge detection texture feature coding image segmentation fpga |
| url | https://doi.org/10.1515/jisys-2014-0075 |
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