Home /Research /Automatic Classification of Wood Texture Using Local Binary Pattern & Fuzzy K-Nearest Neighbor
OTHER

Automatic Classification of Wood Texture Using Local Binary Pattern & Fuzzy K-Nearest Neighbor

Ismail Mohd Khairuddin, Ali Abuassal, Ali Abdelrahim, Amar Faiz Zainal Abidin, Syahrul Hisham Mohamad, Mutaz Alsawi, Nur Anis Nordin, Hazriq Izzuan Jaafar

Year
2014
Citations
2

Abstract

The price of the wood according to the type of wood. Classification of the woods can be done by studying its texture. This paper introduces Fuzzy k Nearest Neighbor to classify 25 types of wood. The woods images have been taken from the Wood Database of the Centre for Artificial Intelligence & Robotics, Universiti Teknologi Malaysia. The features of wood images are extracted using Local Binary Pattern. The results of this paper shows improvement in wood classification compare to the previous literature.

Keywords

Artificial intelligencePattern recognition (psychology)k-nearest neighbors algorithmLocal binary patternsTexture (cosmology)Fuzzy logicBinary numberComputer scienceMathematicsImage (mathematics)

Related papers

Browse all OTHER papers