Zenian, S. and Ahmad, T. and Idris, A. (2016) Edge detection of flat electroencephalography image via classical and fuzzy approach. In: 2nd International Conference on Soft Computing in Data Science, SCDS 2016, 21 - 22 Sept 2016, Kuala Lumpur, Malaysia.
Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....
Abstract
Edge detection is a crucial step in image processing in order to mark the point where the light intensity changed significantly. It is widely used to detect gray-scale and colour images in various fields such as medical image processing, machine vision system and remote sensing. The classical edge detectors such as Prewitt, Robert, and Sobel are quite sensitive towards noise and sometimes inaccurate. In this paper, the boundary of the epileptic foci of Flat EEG (fEEG) is determined by implementing some of the methods ranging from classical to fuzzy approach. There are two methods being applied for the fuzzy edge detector technique which are Minimum Constructor and Maximum Constructor methods; and Fuzzy Mathematical Morphology approach.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Edge detection, Flat eeg, Fuzzy image, Fuzzy set, Grayscale morphology, Mathematical morphology |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Science |
| ID Code: | 73660 |
| Deposited By: | Mohd Zulaihi Zainudin |
| Deposited On: | 20 Nov 2017 08:43 |
| Last Modified: | 20 Nov 2017 08:43 |
Repository Staff Only: item control page
