Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM)
Recent years, vision-based fruit grading system is gaining importance in fruit classification process.In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically.Image segmentation is a process that divides a digital image into separate region...
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2016
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| Online Access: | https://repo.uum.edu.my/id/eprint/20635/1/JT%20SE%2078%206%E2%80%935%20%202016%2013%E2%80%9320.pdf |
| _version_ | 1803627939090137088 |
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| author | Hambali, Hamirul ’Aini Syed Abdullah, Sharifah Lailee Jamil, Nursuriati Harun, Hazaruddin |
| author_facet | Hambali, Hamirul ’Aini Syed Abdullah, Sharifah Lailee Jamil, Nursuriati Harun, Hazaruddin |
| author_sort | Hambali, Hamirul ’Aini |
| collection | UUM |
| description | Recent years, vision-based fruit grading system is gaining importance in fruit classification process.In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically.Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques.However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface.The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis.Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy.The developed algorithm is an integration of modified thresholding and adaptive K-means method.The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour.The results showed that the innovative method is able to segment the fruits images with high accuracy value. |
| first_indexed | 2024-07-04T06:14:01Z |
| format | Article |
| id | uum-20635 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:14:01Z |
| publishDate | 2016 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | uum-206352017-01-18T03:06:45Z https://repo.uum.edu.my/id/eprint/20635/ Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) Hambali, Hamirul ’Aini Syed Abdullah, Sharifah Lailee Jamil, Nursuriati Harun, Hazaruddin QA75 Electronic computers. Computer science Recent years, vision-based fruit grading system is gaining importance in fruit classification process.In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically.Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques.However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface.The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis.Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy.The developed algorithm is an integration of modified thresholding and adaptive K-means method.The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour.The results showed that the innovative method is able to segment the fruits images with high accuracy value. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/20635/1/JT%20SE%2078%206%E2%80%935%20%202016%2013%E2%80%9320.pdf Hambali, Hamirul ’Aini and Syed Abdullah, Sharifah Lailee and Jamil, Nursuriati and Harun, Hazaruddin (2016) Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM). Jurnal Teknologi, 78 (6-5). pp. 13-20. ISSN 0127-9696 http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/8993 |
| spellingShingle | QA75 Electronic computers. Computer science Hambali, Hamirul ’Aini Syed Abdullah, Sharifah Lailee Jamil, Nursuriati Harun, Hazaruddin Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title | Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title_full | Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title_fullStr | Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title_full_unstemmed | Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title_short | Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM) |
| title_sort | intelligent segmentation of fruit images using an integrated thresholding and adaptive k means method tsnkm |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/20635/1/JT%20SE%2078%206%E2%80%935%20%202016%2013%E2%80%9320.pdf |
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