Empirical Evaluation of Suitable Segmentation Algorithms for IR Images
| dc.category | Journal Article | |
| dc.contributor.author | Padmavathi, G | |
| dc.contributor.author | Subashini, P | |
| dc.date.accessioned | 2017-04-18T19:03:04Z | |
| dc.date.available | 2017-04-18T19:03:04Z | |
| dc.date.issued | 2010 | |
| dc.department | Computer Science | en_US |
| dc.description.abstract | Image segmentation is the first stage of processing in many practical computer vision systems. Development of segmentation algorithms has attracted considerable research interest, relatively little work has been published on the subject of their evaluation. Hence this paper enumerates and reviews mainly the image segmentation algorithms namely Otsu, Fuzzy C means. Global Active Contour / Snake model and Watershed. These suitable segmentation methods arc implemented for IR images and arc evaluated based on the parameters. The parameters arc Variation Of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). The objective of the paper is to identify the best segmentation algorithm that is suitable for IR images. From the experimentation and evaluation it is observed that the Global Active Contour/Snakc model works better compared to other methods for IR images. | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/3231 | |
| dc.lang | English | en_US |
| dc.publisher.name | International Journal ot Computer Science Issues | en_US |
| dc.publisher.type | International | en_US |
| dc.title | Empirical Evaluation of Suitable Segmentation Algorithms for IR Images | en_US |
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