1. Image Preprocessing Assessment Detecting Low Contrast Regions Under non-Homogeneous Light Conditions Camilo Vargas 1, Jeyson Molina 1, John W. Branch 1, Alejandro Restrepo 2 1 Escuela de Sistemas, Facultad de Minas, Universidad Nacional de Colombia Sede Medellín {cjvargas, jjmolinac, jwbranch}@unal.edu.co 2 Instituto Tecnológico Metropolitano, Medellín, Colombia alejandrorestrepo@itm.edu.co 1st PSIVT Workshop on Quality Assessment and Control by Image and Video Analysis

2. Agenda 1. Motivation / application 2. Assessment 3. Methods and procedures 4. Results 5. Conclusions 6. References

3. 1. Motivation

4. Motivation / application: – Automated visual inspection. – Low-contrast flaw detection. – Flaw detection on dentures. – LCDs [], plastic surfaces and leather []. Imagen diente

5. Artificial tooth Flaw Fig 2. Dentures flaw detection prototype Motivation / application

6. 2. Assessment

7. Automated visual inspection: Specific problem: Low-contrast flaw detection under uneven lighting images (performance). acquisition Pre-processing Segmentation Feature extraction Classification Specific problem Fig 3. Automated visual inspection process

8. Assessment – Measure the flaw detection process performance with a without pre-processing. Segmentation Feature extraction Classification Preprocessed image set Non-preprocessed image set Results: Segmentation Classification Total Results: Segmentation Classification Total Fig 4. Assessment process

9. 3. Methods and procedures

10. Pre-processing – Unsharp mask – Total variation denoising – Contrast stretching Segmentation – Tophat transform Feature extraction Clasification (SVM)

11. Preprocessing: Unsharp Mask – Substracts a fraction of the blurred version of the input image to the original input image itself. Input image Unsharp mask (λ = 0.6, sigma = 7 ) Fig 5. Unsharp masking results

12. Input image (Sharpen) Denoised image w=10, iter = 100 Fig 6. T.V. Denoising results

13. Input image (Denoised) Contrast enhanced image (a=40, b=210) Fig 7. Contrast stretching results

14. Preprocessing: Results Fig 8. Preprocessing Results Unsharp mask, TV. denoising, and contrast stretching Input imagePreprocessed image

15. Fig 9. Tophat segmentationFig 10. Parameter selection (TPR/FPR)

16. Feature extraction: – 52 Features: Intensity levels, geometry, contrast and texture features. Principal component analisys. SMOTE: to balance feature data base generating flaw instances. Classification: Support vector machine – Supervised learning and cross validation (10 fold).

17. 4. Results

18. Assessment: – 2 image sets, preprocessed and non-preprocessed images (40 images each set). – 73 flaw regions. Detection process Results: Segmentation Classification Total Results: Segmentation Classification Total Fig 11. Assessment process Preprocessed images set Non- preprocessed images set

19. Segmentation - Low-contrast, uneven lighting, fixed Tophat kernel size, variable flaws regions size. Flaw regions: True positive False alarm region: False positive Flaw regions segmented False alarm regions segmented Total segmented regions Non-preprocessed image set7796173 Preprocessed image set127363490 Table 1. Segmented regions Fig 12. Segmentation results

20. Confusion matrixes and accuracy Acc = (TP+TN) / (TP+TN+FP+FN) Non-preprocessed images classifier False alarm regionFlaw region False alarm region951 Flaw region374 Preprocessed images classifier False alarm regionFlaw region False alarm region34716 Flaw region14113 97.68% 93.87% Table 2. Non-preprocessed images classifier confusion matrix Table 3. Preprocessed images classifier confusion matrix

21. Flaw samples correctly detected – Detection results vs manual segmentations. Detected flaw samples Undetected flaw samples Percentage of flaw samples detected Non-preprocessed images 291172.5% Preprocessed images 34685% Table 4. Overall detection process performance

22. 5. Conclusions

23. For the given low-contrast and uneven ligthing circumstances specialized preprocessing and segmentation techniques are required. 12.5% increase on the overall detection process performance is noticed. The preprocessing impacts the detection process mainly on the segmentation stage.

24. 6. References 1.D. M. Tsai, M. C. Chen, W. C. Li, W. Y. Chiu, 2012, “A fast regularity measure for surface defect detection,” Machine Vision and Applications, Vol. 23, pp. 869-886. (SCI) 2.Liang-Chia Chen; Chih-Hung Chien; Xuan-Loc Nguyen; (2013) An effective image segmentation method for noisy low-contrast unbalanced background in Mura defects using balanced discrete-cosine-transfer (BDCT); Precision Engineering Vol 37, Issue 2, p336–344 3.Rodríguez, Juan Carlos; Molina, Jason; Atencio, Pedro; Branch, John W.; Restrepo Alejandro. “Anisotropic filtering assessment applied on superficial defects enhancement under non homogenous light conditions”; Revista Avances en Sistemas e Informática, Vol. 8 No 3. 57 – 62 (2011) 4.Kim S; Allebach JP; “Optimal unsharp mask for image sharpening and noise removal”. J. Electron. Imaging. 14(2), (2005) 5.NH Mahmood; MRM Razif; MTAN Gany; “Comparison between Median, Unsharp and Wiener filter and its effect on ultrasound stomach tissue image segmentation for Pyloric Stenosis”; International Journal of Applied Science and Technology Vol. 1 No. 5. 218-226 (2011)

25. 6. References 1.Leonid I. Rudin; Stanley Osher; Emad Fatemi; “Nonlinear total variation based noise removal algorithms” Physica D: Nonlinear Phenomena, Volume 60, Issues 1–4. 259–268 (1992) 2.Antonin Chambolle; “An Algorithm for Total Variation Minimization and Applications”; Journal of Mathematical Imaging and Vision. Vol. 20, Issue 1-2. 89-97 (2004) 3.Svante Wold; Kim Esbensen; Paul Geladi;”Principal Component Analysis”. Chemometrics and intelligent laboratory systems Vol 2 No 1. 37-52 (1987) 4.Nitesh V. Chawla; Kevin W. Bowyer; Lawrence O. Hall; W. Philip Kegelmeyer; “SMOTE: Synthetic Minority Over-sampling Technique”. Journal of Artificial Intelligence Research 16. 321–357 (2002) 5.Corinna Cortes; Vladimir Vapnik; “Support-Vector Networks”. Machine Leaming Vol. 20. 273-297 (1995). 6.Domingo Mery, “Crossing Line Profile: A New Approach to Detecting Defects in Aluminium Die Casting” SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis, Springer-Verlag Berlin. 725- 732 (2003)

26. Gracias por su atención! Agradecimientos a la Facultad de Minas de la Universidad Nacional de colombia, al SENA y a New Stetic