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https://ptsldigital.ukm.my/jspui/handle/123456789/476416Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Siti Nurul Huda Sheikh Abdullah Dr. | |
| dc.contributor.author | Mohammed Ahmed Talab (P63369) | |
| dc.date.accessioned | 2023-10-06T09:18:05Z | - |
| dc.date.available | 2023-10-06T09:18:05Z | - |
| dc.date.issued | 2013-10-11 | |
| dc.identifier.other | ukmvital:85144 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476416 | - |
| dc.description | The shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. In these cases, the descriptors need to have strong local nature in order to encode the information that distinguishes them. Regardless of its intricacies, Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. However, edge direction matrixes (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main idea behind this methodology is the need of improved recognition capabilities, a goal achieved by the combinative use of these descriptors. This collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using multiple classifier approaches such as random forest and multi-layer perceptron neural network, the proposed combinative descriptor are compared with the state of the art combinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS), LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1 for shape, KTH-TIPS image for texture, Enghlishfnt for character and Arabic calligraphy for texture. The experiments have shown the superiority of the introduced descriptor over the GLCM with EDMS, LBP and moment invariants and other well-known descriptor such as Scale Invariant Feature Transform from the literature.,Master / Sarjana | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Computer vision. | |
| dc.title | Edge direction matrixes-local binary patterns descriptor for shape pattern recognition | |
| dc.type | theses | |
| dc.format.pages | 89 | |
| dc.identifier.callno | TA1634.T339 2013 3 | |
| dc.identifier.barcode | 002193 | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_85144+SOURCE1+SOURCE1.0.PDF Restricted Access | 2.79 MB | Adobe PDF |  View/Open |
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