Edge detection of aerial images using artificial bee colony algorithm
Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to...
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| Format: | Article |
| Language: | English |
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Kyrgyz Turkish Manas University
2022-06-01
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| Series: | MANAS: Journal of Engineering |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/2174978 |
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| author | Nurdan Akhan Baykan Elif Deniz Yelmenoglu |
| author_facet | Nurdan Akhan Baykan Elif Deniz Yelmenoglu |
| author_sort | Nurdan Akhan Baykan |
| collection | DOAJ |
| description | Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images. |
| format | Article |
| id | doaj-art-7edc6c49f25949198b6db81b450e3204 |
| institution | Kabale University |
| issn | 1694-7398 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Kyrgyz Turkish Manas University |
| record_format | Article |
| series | MANAS: Journal of Engineering |
| spelling | doaj-art-7edc6c49f25949198b6db81b450e32042025-02-03T12:02:40ZengKyrgyz Turkish Manas UniversityMANAS: Journal of Engineering1694-73982022-06-01101738010.51354/mjen.10534461437Edge detection of aerial images using artificial bee colony algorithmNurdan Akhan Baykan0https://orcid.org/0000-0002-4289-8889Elif Deniz Yelmenoglu1https://orcid.org/0000-0002-3645-3445KONYA TEKNİK ÜNİVERSİTESİIŞIK ÜNİVERSİTESİEdge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.https://dergipark.org.tr/en/download/article-file/2174978image processingedge detectionartificial bee colony optimizationaerial images |
| spellingShingle | Nurdan Akhan Baykan Elif Deniz Yelmenoglu Edge detection of aerial images using artificial bee colony algorithm MANAS: Journal of Engineering image processing edge detection artificial bee colony optimization aerial images |
| title | Edge detection of aerial images using artificial bee colony algorithm |
| title_full | Edge detection of aerial images using artificial bee colony algorithm |
| title_fullStr | Edge detection of aerial images using artificial bee colony algorithm |
| title_full_unstemmed | Edge detection of aerial images using artificial bee colony algorithm |
| title_short | Edge detection of aerial images using artificial bee colony algorithm |
| title_sort | edge detection of aerial images using artificial bee colony algorithm |
| topic | image processing edge detection artificial bee colony optimization aerial images |
| url | https://dergipark.org.tr/en/download/article-file/2174978 |
| work_keys_str_mv | AT nurdanakhanbaykan edgedetectionofaerialimagesusingartificialbeecolonyalgorithm AT elifdenizyelmenoglu edgedetectionofaerialimagesusingartificialbeecolonyalgorithm |