A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
A Novel Approach to Real-time Bilinear Interpolation
DELTA '04 Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications
Latent defect screening for high-reliability glass-ceramic multichip module copper interconnects
IBM Journal of Research and Development - POWER5 and packaging
Defect detection on semiconductor wafer surfaces
Microelectronic Engineering
Texture edge detection by feature encoding and predictive model
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
An anisotropic diffusion-based defect detection for low-contrast glass substrates
Image and Vision Computing
WSEAS Transactions on Computer Research
Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion
Image and Vision Computing
Surface Defect Characterization in Polishing Process Using Contour Dispersion
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Stochastic texture analysis for monitoring stochastic processes in industry
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Texture exemplars for defect detection on random textures
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery
IEEE Transactions on Image Processing
Image coding using wavelet transform
IEEE Transactions on Image Processing
Salient features selection for multiclass texture classification
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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Solar power is an attractive alternative source of electricity. Multicrystalline solar cells dominate the market share owing to their lower manufacturing costs. The surface quality of a solar wafer determines the conversion efficiency of the solar cell. A multicrystalline solar wafer surface contains numerous crystal grains of random shapes and sizes in random positions and directions with different illumination reflections, therefore resulting in an inhomogeneous texture in the sensed image. This texture makes the defect detection task extremely difficult. This paper proposes a wavelet-based discriminant measure for defect inspection in multicrystalline solar wafer images. The traditional wavelet transform techniques for texture analysis and surface inspection rely mainly on the discriminant features extracted in individual decomposition levels. However, these techniques cannot be directly applied to solar wafers with inhomogeneous grain patterns. The defects found in a solar wafer surface generally involve scattering and blurred edges with respect to clear and sharp edges of crystal grains in the background. The proposed method uses the wavelet coefficients in individual decomposition levels as features and the difference of the coefficient values between two consecutive resolution levels as the weights to distinguish local defects from the crystal grain background, and generates a better discriminant measure for identifying various defects in the multicrystalline solar wafers. Experimental results have shown the proposed method performs effectively for detecting fingerprint, contaminant, and saw-mark defects in solar wafer surfaces.