A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information
Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clust...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/12/4209 |
| _version_ | 1827689341307060224 |
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| author | Kemeng Bai Jianzhong Wang Hongfeng Wang |
| author_facet | Kemeng Bai Jianzhong Wang Hongfeng Wang |
| author_sort | Kemeng Bai |
| collection | DOAJ |
| description | Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking. |
| first_indexed | 2024-03-10T10:15:27Z |
| format | Article |
| id | doaj.art-3905b073c460434ba0f1e380ee39fc3d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:15:27Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3905b073c460434ba0f1e380ee39fc3d2023-11-22T00:49:27ZengMDPI AGSensors1424-82202021-06-012112420910.3390/s21124209A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed InformationKemeng Bai0Jianzhong Wang1Hongfeng Wang2School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaPupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.https://www.mdpi.com/1424-8220/21/12/4209pupil detectionimage segmentationfuzzy clusteringlocal featureshead-mounted eye-tracking system |
| spellingShingle | Kemeng Bai Jianzhong Wang Hongfeng Wang A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information Sensors pupil detection image segmentation fuzzy clustering local features head-mounted eye-tracking system |
| title | A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information |
| title_full | A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information |
| title_fullStr | A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information |
| title_full_unstemmed | A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information |
| title_short | A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information |
| title_sort | pupil segmentation algorithm based on fuzzy clustering of distributed information |
| topic | pupil detection image segmentation fuzzy clustering local features head-mounted eye-tracking system |
| url | https://www.mdpi.com/1424-8220/21/12/4209 |
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