KSEM 2017: Knowledge Science, Engineering and Management pp 224-236 | Cite as
A Novel Blemish Detection Algorithm for Camera Quality Testing
Abstract
In the camera manufacturing, there exist dusts, fingerprints, and water spots on the image sensor and lens. Hence, the resultant effects of darker region is called blemish, which causes a significant reduction in camera quality. The shapes of blemishes are diverse and irregular. Traditional method detects blemishes using image median filtering in a single direction which leads to false alarm and mis-detection for images with high level of noises. Thus, we present a novel filtering method for blemish detection, which utilizes four directional filters in the 0, 45, 90, 135 degrees directions. Compared to the conventional single direction filter, the multidirectional filters take into account more spatial information to more accurately detect blemishes for both weak and strong blemishes. Moreover, the proposed method uses a new adaptive threshold to better accommodate different image noise levels automatically. Experimental results on two batches of production samples (600 images) show the effectiveness of the proposed method over the conventional method.
Keywords
Blemish detection Camera manufacturing Multidirectional filterNotes
Acknowledgments
This work was supported in part by the Shenzhen Emerging Industries of the Strategic Basic Research Project (No. JCYJ20160226191842793), and the National Natural Science Foundation of China (No. 61602312, 61602314, 61620106008).
References
- 1.Cazier, R., Voss, S.D., Yost, J.: Blemish detection and notification in an image capture device. United States Patent No. 8982262 B2 (2015)Google Scholar
- 2.Kim, S., Kang, T., Jeong, D.: Region mura detection using efficient high pass filtering based on fast average operation. In: Proceedings of the 17th World Congress, International Federation of Automatic Control, vol. 17 (2008)Google Scholar
- 3.Lin, H.D., Chiu, Y.S.P.: Computer-aided quality system for visual blemish inspection of epoxy packages. Sci. Iranica 18, 1591–1599 (2011)CrossRefGoogle Scholar
- 4.Pietikainen, M., Ojala, T., Silven, O.: Approaches to texture-based classification, segmentation and surface inspection. In: Handbook of Pattern Recognition and Computer Vision, pp. 711–736 (1999)Google Scholar
- 5.Tsai, D.M., Chuang, S.T.: ID-based defect detection in patterned TFT-LCD panels using characteristic fractal dimension and correlations. Mach. Vis. Appl. 20, 423–434 (2009)CrossRefGoogle Scholar
- 6.Lin, H.-D., Chung, C.-Y., Chiu, S.W.: Computer-aided vision system for surface blemish detection of LED chips. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 525–533. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71629-7_59 CrossRefGoogle Scholar
- 7.Chiu, Y.S., Lin, H.D.: An innovative blemish detection system for curved LED lenses. Expert Syst. Appl. 40, 471–479 (2013)CrossRefGoogle Scholar
- 8.Pratt, W.K., Sawkar, S.S., O’Reilly, K.: Automatic blemish detection in liquid crystal flat panel displays. In: Proceedings of SPIE-The International Society (1998)Google Scholar
- 9.Reza, M.T.: Vignetting artifact reduction in an efficient way for digital camera image. Tampere University of Technology (2008)Google Scholar
- 10.Zhang, H., Shen, S., McAllister, I.A.: Camera blemish defects detection. United States Patent No. 8797429 B2 (2013)Google Scholar
- 11.Lepisto, L., Nikkanen, J., Suksi, M.: Blemish detection in camera production testing using fast difference filtering. J. Electron. Imaging 18, 020501 (2009)CrossRefGoogle Scholar
- 12.Boncelet, C., Bovik, A.C.: Handbook of Image and Video Processing. Academic Press, Cambridge (2005)Google Scholar