This paper addresses the issue of extracting textural feature for timber defect detection. Statistical features based on spatial dependence matrix are extracted for both classes; clear wood and defect. Instead of using the classical directional matrices, rotation invariant spatial dependence matrix formulation is applied to ensure accurate detection regardless of the timber feed direction. Hotelling T-Squared test is used to measure significance difference of mean between feature distributions of the two classes. The result will give some indication to whether the features extracted are sufficient/good enough to be used in future classification stage
This thesis discusses the application of the image analysis methods for the cross section surface im...
A key to wood identification is the distinguishable features found on the cross-sectional surface of...
To improve the sawyer's ability to process hardwood logs and stems, and thereby generate a higher va...
Feature extraction is unquestionably an important process in a pattern recognition system. A defined...
This paper presents performance evaluation of texture features based on orientation independent Grey...
This paper presents performance evaluation of texture features based on orientation independent Grey...
Selecting important features in classifying wood defects remains a challenging issue to the automate...
Selecting important features in classifying wood defects remains a challenging issue to the automat...
Substantial research effort has been done in the automation of timber defect detection to improve th...
Automated inspection has proven to be of great importance in increasing the quality of timber produc...
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...
Extensive research has been done on the automation of wood defect detection, to improve the quality ...
This paper presents a new method for wood defect detection. It can solve the over-segmentation probl...
This paper describes a prototype computer vision system based on texture analysis that can automatic...
This thesis discusses the application of the image analysis methods for the cross section surface im...
A key to wood identification is the distinguishable features found on the cross-sectional surface of...
To improve the sawyer's ability to process hardwood logs and stems, and thereby generate a higher va...
Feature extraction is unquestionably an important process in a pattern recognition system. A defined...
This paper presents performance evaluation of texture features based on orientation independent Grey...
This paper presents performance evaluation of texture features based on orientation independent Grey...
Selecting important features in classifying wood defects remains a challenging issue to the automate...
Selecting important features in classifying wood defects remains a challenging issue to the automat...
Substantial research effort has been done in the automation of timber defect detection to improve th...
Automated inspection has proven to be of great importance in increasing the quality of timber produc...
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...
Extensive research has been done on the automation of wood defect detection, to improve the quality ...
This paper presents a new method for wood defect detection. It can solve the over-segmentation probl...
This paper describes a prototype computer vision system based on texture analysis that can automatic...
This thesis discusses the application of the image analysis methods for the cross section surface im...
A key to wood identification is the distinguishable features found on the cross-sectional surface of...
To improve the sawyer's ability to process hardwood logs and stems, and thereby generate a higher va...