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Texture analysis and discrimination in additive noise
Computer Vision, Graphics, and Image Processing
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Texture Classification Using Windowed Fourier Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast fixed-point algorithm for independent component analysis
Neural Computation
Graphical Models and Image Processing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A golden-template self-generating method for patterned wafer inspection
Machine Vision and Applications
Wavelet methods for texture defect detection
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Edge Detection and Texture Segmentation Based on Independent Component Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Independent Component Analysis of Textures
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Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Segmentation of Gabor-filtered textures using deterministic relaxation
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Spatial Filtering with Multi-scale Segmentation Based on Gaussian Function
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
A new classifier for facial expression recognition: fuzzy buried Markov model
Journal of Computer Science and Technology
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In this paper, we propose a fast self-comparison scheme for defect detection in structural surfaces containing periodic complicated patterns. It works directly on a one-dimensional line image, instead of a two-dimensional array image, that contains a periodic pattern in the line. The proposed self-comparison scheme is simply carried out by dividing a sensed line image into two segments of equal length. Since the line image contains a periodic pattern, the two divided segments are only translated versions to each other. In this study, an independent component analysis (ICA) model is proposed to obtain the de-mixing matrix that can recover the translation between the two divided segments. The proposed ICA model directly measures the independency of signals by minimizing the difference between the joint probability density function (PDF) and the product of marginal PDFs, in which the PDFs are estimated by relative frequency distributions. The particle swarm optimization (PSO) algorithm is used to search for the de-mixing matrix. The proposed ICA model can effectively separate highly correlated signals, and is well suited for translation recovery between two signals with the same periodic pattern. In the detection stage, each line image is first divided into two segments, and the de-mixing matrix learned off-line from a defect-free line image is used to recover the signals with well aligned translation. The normalized cross-correlation is adopted to measure the similarity between two compared segments. Since the de-mixing matrix is only of a small size of 2x2, the proposed method in the detection stage is very computationally efficient. The performance of the proposed method is demonstrated with test samples of TFT-LCD panels and color filters found in LCD manufacturing. Experimental results have shown that the proposed self-comparison scheme can effectively and efficiently detect the presence of defects in periodically patterned surfaces.