Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. Howeve...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2012
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| id |
my-usm-ep.41804 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-usm-ep.418042019-04-12T05:26:22Z Enhanced Clustering Algorithms For Gray-Scale Image Segmentation 2012-04 Siddiqui, Fasahat Ullah TK1-9971 Electrical engineering. Electronics. Nuclear engineering The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). 2012-04 Thesis http://eprints.usm.my/41804/ http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Eletrik & Elektronik |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
TK1-9971 Electrical engineering Electronics Nuclear engineering |
| spellingShingle |
TK1-9971 Electrical engineering Electronics Nuclear engineering Siddiqui, Fasahat Ullah Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| description |
The clustering algorithms are widely used as an unsupervised method for image segmentation in medical diagnosis, satellite imaging and biometric systems. The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. However, in some cases the conventional clustering algorithms introduce over-segmentation problems and unable to preserve the region of interest (i.e. objects). |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Siddiqui, Fasahat Ullah |
| author_facet |
Siddiqui, Fasahat Ullah |
| author_sort |
Siddiqui, Fasahat Ullah |
| title |
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_short |
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_full |
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_fullStr |
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_full_unstemmed |
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation |
| title_sort |
enhanced clustering algorithms for gray-scale image segmentation |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Kejuruteraan Eletrik & Elektronik |
| publishDate |
2012 |
| url |
http://eprints.usm.my/41804/1/FASAHAT_ULLAH_SIDDIQUI.pdf |
| _version_ |
1747820973471563776 |