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 wood’s 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
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic c...
The Classification of wood type can be done by studying the texture of the wood knot. This paper pro...
Abstract: Currently, the presence of wood is becoming increasingly scarce. In addition, the recognit...
Woods species recognition is a texture classification difficulty that has been studied by many resea...
Effective statistical feature extraction and classification are important in image-based automatic i...
A cascaded wood species recognition system using simple statistical properties of the wood texture i...
The recognition of wood species is needed is many areas like construction industry, furniture manufa...
An automated wood texture recognition system of 48 tropical wood species is presented. For each wood...
An improved wood texture feature description algorithm of Local Binary Pattern operator is proposed....
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper proposed an application of Binary Particle Swarm Optimization in automatic classification...
This paper proposed an application of Binary Particle Swarm Optimization in automatic classification...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic c...
The Classification of wood type can be done by studying the texture of the wood knot. This paper pro...
Abstract: Currently, the presence of wood is becoming increasingly scarce. In addition, the recognit...
Woods species recognition is a texture classification difficulty that has been studied by many resea...
Effective statistical feature extraction and classification are important in image-based automatic i...
A cascaded wood species recognition system using simple statistical properties of the wood texture i...
The recognition of wood species is needed is many areas like construction industry, furniture manufa...
An automated wood texture recognition system of 48 tropical wood species is presented. For each wood...
An improved wood texture feature description algorithm of Local Binary Pattern operator is proposed....
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper presents an analysis of the statistical texture representation of the Local Binary Patter...
This paper proposed an application of Binary Particle Swarm Optimization in automatic classification...
This paper proposed an application of Binary Particle Swarm Optimization in automatic classification...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, nov...
Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic c...