Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion

Tsai, Du‐Ming; Chang, C. M.; Shen, Chao

doi:10.1016/j.imavis.2009.08.001

Cited by 89 publications

(49 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this subsection, the performance of proposed method is compared visually and numerically with related methods, such as anisotropic diffusion model based microcrack detection and wavelet-based defect detection methods [28,32]. These algorithms are implemented with MATLAB.…”

Section: Comparison With Anisotropic Diffusion and Wavelet Based Methodsmentioning

confidence: 99%

“…Tsai et al presented the anisotropic diffusion model based micro-crack detection method. Their method works successfully for detecting micro-cracks; however, it cannot be extended to detect all types of defects, such as fingerprint, oil stain, and contamination [28]. On the contrary, Li and Tsai presented wavelet based defect detection method and their method works successfully for detecting other defect types; however, it cannot be extended to detect the micro-crack [32].…”

Section: Comparison With Anisotropic Diffusion and Wavelet Based Methodsmentioning

confidence: 99%

“…The polycrystalline solar wafer has heterogeneously textured surface in the digital image. It has multiple grains that have random sizes, shapes, and variations in various directions on the surface [28,29]. Therefore, homogeneously textured surface-based conventional methods cannot be applied to this solar wafer inspection system.…”

Section: Introductionmentioning

confidence: 99%

“…Tsai et al presented various defect detection methods. Anisotropic diffusion model based method was applied to detecting micro-cracks [28]. Meanshift based method was applied to detecting defects [29].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Defect detection of polycrystalline solar wafers using local binary mean

Rheem

2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Polycrystalline solar wafers consist of various crystals and their surfaces have heterogeneous textures. The conventional defect detection methods cannot be applied to their solar wafers. In this paper, we propose a concept of local binary mean and its optimization method for selecting optimal threshold T . The input image is broken down into a set of K patch images. Each patch image is used to calculate its local binary mean. The local binary mean value is used as the discrimination measure for detecting defects. Experimental results show that our proposed method achieves a detection rate of 91∼94 %. Compared with related defect detection methods, the proposed method has the advantage of detecting various kinds of low gray-level defects such as micro-cracks, fingerprints, and contaminations simultaneously.

show abstract

Section: Comparison With Anisotropic Diffusion and Wavelet Based Methodsmentioning

confidence: 99%

Section: Comparison With Anisotropic Diffusion and Wavelet Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Defect detection of polycrystalline solar wafers using local binary mean

Rheem

2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Tsai and Chao's method was superior to Gaussian and median filters because it did not falsely detect defects in clear surfaces and was also accurate at detecting them without including too many noisy pixels. The same technique, anisotropic diffusion, was used by Tsai et al for the inspection of micro-cracks in heterogeneously textured solar wafers [26]. They modified the approach by subtracting the diffused image from the original gray-level image.…”

Section: Introductionmentioning

confidence: 99%

Feature selection for surface defect classification of extruded aluminum profiles

Chondronasios

Иван

Jordanov

2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.

show abstract

Photovoltaics literature survey (No. 79)

Shrestha

2010

Progress in Photovoltaics

View full text Add to dashboard Cite

In order to help keep readers up‐to‐date in the field each issue of Progress in Photovoltaics will contain a list of recently published journal articles most relevant to its aims and scope. This list is drawn from an extremely wide range of journals, including IEEE Transactions on Electron Devices, Journal of Applied Physics, Applied Physics Letters, Progress in Photovoltaics, and Solar Energy Materials and Solar Cells. To assist the reader, the list is separated into broad categories, but please note that these classifications are by no means strict. Also note that inclusion in the list is not an endorsement of a paper's quality.

show abstract

Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion

Cited by 89 publications

References 37 publications

Defect detection of polycrystalline solar wafers using local binary mean

Defect detection of polycrystalline solar wafers using local binary mean

Feature selection for surface defect classification of extruded aluminum profiles

Photovoltaics literature survey (No. 79)

Contact Info

Product

Resources

About