Abstract
On the surface of the quad flat non-lead (QFN) dark-filed images, noise pixels (including textures produced in the molding process) obstruct the defect inspection. To extract defects from QFN surface, a novel method based on texture detection and region of interest selection is proposed. Firstly, a QFN texture direction detector is proposed. Secondly, multilevel thresholding method is used to segment QFN images. Thirdly, according to the image level, the bright defects images and the dark defect images are obtained. Then, the region of interest selection method is applied to reserving defects regions and removing QFN textures and noise pixels. Finally, our method extracts defects by combining the bright and dark defects image. The experiments show that the proposed method can extract defects efficiently.
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Yun JP et al (2014) Defect inspection system for steel wire rods produced by hot rolling process. Int J Adv Manuf Technol 70:1625–1634
Song K-C et al (2014) Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference. ISIJ Int 54:2598–2607
Chiu Shih-Wen, Tang Kea-Tiong (2013) Towards a chemiresistive sensor-integrated electronic nose: a review. Sensors 13:14214–14247
Karimi MH, Asemani D (2014) Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation, ISA Trans 53:834–844
Tsai D-M, Luo J-Y (2011) Mean shift-based defect detection in multicrystalline solar wafer surfaces. IEEE Trans Ind Inf 7:125–135
Wang M et al (2009) A fast algorithm for segmenting defects on the surface of QFN packages, International Conference on Information Engineering and Computer Science, ICIECS 2009, IEEE, pp 1–4
Chen K et al (2015) Defects extraction for QFN based on mathematical morphology and modified region growing, IEEE International Conference on Mechatronics and Automation (ICMA), pp 2426–2430
Chen K et al (2014) Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning. J Southeast Univ (English Edn) 30:434–438
Wang Z, Wang ZQ, Mao YW (2002) A description based on texture direction and the clustering and segmentation to directional texture images, J Image Graphics 7:1279–1284
Acknowledgments
This work is supported by the National Nature Science Foundation of China (Grant No.51275090) and the Fundamental Research Funds for the Central Universities and Jiangsu Postgraduate Innovation Program (Grant No.KYLX15_0208).
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© 2016 Springer Science+Business Media Singapore
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Chen, K., Zhang, Z., Chao, Y., He, F., Shi, J. (2016). Defects Extraction for QFN Based on Texture Detection and Region of Interest Selection. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_16
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DOI: https://doi.org/10.1007/978-981-10-1536-6_16
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