Robust clustering algorithms for image segmentation and curve analysis
Data clustering has become one of the most important research areas of pattern recognition. The objective of data clustering is to use the cluster concept to simply the representation of large amount of data objects and generate meaningful clusters for further analysis and interpretation. Such a tec...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2010
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| Online Access: | https://hdl.handle.net/10356/41537 |
| _version_ | 1826110092412452864 |
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| author | Wang, Zhimin |
| author2 | Soh Yeng Chai |
| author_facet | Soh Yeng Chai Wang, Zhimin |
| author_sort | Wang, Zhimin |
| collection | NTU |
| description | Data clustering has become one of the most important research areas of pattern recognition. The objective of data clustering is to use the cluster concept to simply the representation of large amount of data objects and generate meaningful clusters for further analysis and interpretation. Such a technology is useful in many disciplines, such as computational biology, bioinformatics, medical image processing, digital image segmentation, affective computing, real-time market forecast and online document clustering search engine. There are several crucial steps in a pattern analysis system based on data clustering methodologies. These include data collection, feature extraction/selection, clustering strategy, and clustering output interpretation. Among these issues, the clustering method is an especially important one. Robustness, efficiency, extendibility, and universality of a data clustering analysis system are usually determined by the data clustering method. However, there is no universal clustering technique that is always applicable for uncovering the variety of structures present in the data sets. This thesis focuses on the development of adaptive, robust, and generalized data clustering methods for real applications. |
| first_indexed | 2024-10-01T02:28:47Z |
| format | Thesis |
| id | ntu-10356/41537 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:28:47Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/415372023-07-04T16:53:32Z Robust clustering algorithms for image segmentation and curve analysis Wang, Zhimin Soh Yeng Chai Song Qing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Data clustering has become one of the most important research areas of pattern recognition. The objective of data clustering is to use the cluster concept to simply the representation of large amount of data objects and generate meaningful clusters for further analysis and interpretation. Such a technology is useful in many disciplines, such as computational biology, bioinformatics, medical image processing, digital image segmentation, affective computing, real-time market forecast and online document clustering search engine. There are several crucial steps in a pattern analysis system based on data clustering methodologies. These include data collection, feature extraction/selection, clustering strategy, and clustering output interpretation. Among these issues, the clustering method is an especially important one. Robustness, efficiency, extendibility, and universality of a data clustering analysis system are usually determined by the data clustering method. However, there is no universal clustering technique that is always applicable for uncovering the variety of structures present in the data sets. This thesis focuses on the development of adaptive, robust, and generalized data clustering methods for real applications. DOCTOR OF PHILOSOPHY (EEE) 2010-07-19T01:44:45Z 2010-07-19T01:44:45Z 2009 2009 Thesis Wang, Z. (2009). Robust clustering algorithms for image segmentation and curve analysis. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/41537 10.32657/10356/41537 en 188 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Wang, Zhimin Robust clustering algorithms for image segmentation and curve analysis |
| title | Robust clustering algorithms for image segmentation and curve analysis |
| title_full | Robust clustering algorithms for image segmentation and curve analysis |
| title_fullStr | Robust clustering algorithms for image segmentation and curve analysis |
| title_full_unstemmed | Robust clustering algorithms for image segmentation and curve analysis |
| title_short | Robust clustering algorithms for image segmentation and curve analysis |
| title_sort | robust clustering algorithms for image segmentation and curve analysis |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | https://hdl.handle.net/10356/41537 |
| work_keys_str_mv | AT wangzhimin robustclusteringalgorithmsforimagesegmentationandcurveanalysis |