Introduction—towards a new framework for vision
Geometric invariance in computer vision
Performance characterization in computer vision
CVGIP: Image Understanding
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Toward Bayes-Optimal Linear Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification by multi-model feature integration using Bayesian networks
Pattern Recognition Letters
Invariant Features for Gray Scale Images
Mustererkennung 1995, 17. DAGM-Symposium
Rotation invariant texture classification using even symmetric Gabor filters
Pattern Recognition Letters
Improvement of Histogram-Based Image Retrieval and Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Gaussian MRF Rotation-Invariant Features for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random search techniques for optimization problems
Automatica (Journal of IFAC)
Pattern classification using neural networks
IEEE Communications Magazine
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
A flexible visual inspection system based on neural networks
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
| Hi-index | 0.01 |
Automatic texture defect detection is highly important for many fields of visual inspection. We introduce novel, geometrical texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational, Pade, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained, multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.