| 000 | 01917nam a2200265 a 4500 | ||
|---|---|---|---|
| 001 | vtls000077108 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204602.0 | ||
| 008 | 140328t2013 my a f m 000 0 eng d | ||
| 020 | _aTHE0001770(Local) | ||
| 039 | 9 |
_a201905131230 _byusri _c201404020843 _dFida _c201403280844 _dFida _y201403280844 _zFida |
|
| 040 | _aUMP | ||
| 090 | _aQA76.76.A65 L44 2013 rs Bc. | ||
| 100 | 1 | _aLee, Sai Foong | |
| 245 | 1 | 0 |
_aTexture recognition by using artificial neural network / _cLee Sai Foong |
| 260 |
_aKuantan, Pahang : _bUMP, _c2013 |
||
| 300 |
_axi, 38 p. : _bill. ; _c30 cm. + _e1 CD-ROM |
||
| 502 | _aProject paper (Bachelor of Computer Science (Computer Graphic and Multimedia)) -- Universiti Malaysia Pahang - 2013 | ||
| 504 | _aBibliography : p. 33-34 | ||
| 520 | 3 | _aThis thesis describes the texture recognition by using the Artificial Neural Network (ANN). There are hard to understand on how to perform the texture recognition on any new set of image data. Therefore, to ease up the process on texture recognition, ANN has been chosen as the classifier to enhance the process of the texture recognition. There are thirteen types of Brodatz textures are considered as the dataset for this research and five sets for each type texture with different level of histogram equalized, noise for the training dataset. Backpropagation algorithm is one of the methods for the ANN. After the feature is obtained from the dataset, the feature will be trained and classifier by using theBack-propagation algorithm. All in all, this project will tell us how the Back-propagation classifier help in texture recognition and how to increases the success rate in texture recognition. | |
| 650 | 0 |
_aApplication software _xDevelopment |
|
| 650 | 0 | _aNeural networks | |
| 650 | 0 | _aDecision support systems | |
| 999 |
_aVIRTUA40 _c4587 _d4593 |
||
| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992 | ||