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http://dx.doi.org/10.21219/jitam.2018.25.2.041

Accurate Camera Self-Calibration based on Image Quality Assessment  

Fayyaz, Rabia (Department of Computer Engineering, Hanbat National University)
Rhee, Eun Joo (Department of Computer Engineering, Hanbat National University)
Publication Information
Journal of Information Technology Applications and Management / v.25, no.2, 2018 , pp. 41-52 More about this Journal
Abstract
This paper presents a method for accurate camera self-calibration based on SIFT Feature Detection and image quality assessment. We performed image quality assessment to select high quality images for the camera self-calibration process. We defined high quality images as those that contain little or no blur, and have maximum contrast among images captured within a short period. The image quality assessment includes blur detection and contrast assessment. Blur detection is based on the statistical analysis of energy and standard deviation of high frequency components of the images using Discrete Cosine Transform. Contrast assessment is based on contrast measurement and selection of the high contrast images among some images captured in a short period. Experimental results show little or no distortion in the perspective view of the images. Thus, the suggested method achieves camera self-calibration accuracy of approximately 93%.
Keywords
Image Quality Assessment; Blur detection0; Contrast Assessment; Discrete Cosine Transform; Camera Self-calibration;
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1 Huang, G., Tian, Y., Wang, Y., Yang, Y., and Tai, X., "Self-Recalibration of PTZ Cameras", Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface, Kaifeng, China, 2010, pp. 292-295.
2 Oliveira, J. P., Figueiredo, M. A., and Bioucas-Dias, J. M., "Parametric blur estimation for blind restoration of natural images : linear motion and out-of-focus", IEEE Transactions on Image Processing, Vol. 23, No. 1, 2014, pp. 466-477.   DOI
3 Wilczkowiak, M., Boyer, E., and Sturm, P., "Camera calibration and 3D reconstruction from single images using parallelepipeds", Proceedings of the 8th IEEE International Conference on Computer Vision ICCV 2001, Vancouver, Canada, 2001, pp. 142-148.
4 Wu, J., Cui, Z., Sheng, V. S., Zhao, P., Su, D., and Gong, S., "A Comparative Study of SIFT and its Variants", Measurement Science Review, Vol. 13, No. 3, 2013, pp. 122-131.   DOI
5 Zhang, Z., Camera Calibration in Computer Vision, Boston : Springer, 2014.
6 Zhao, F., Huang, Q., and Gao, W., "Image Matching by Normalized Cross-Correlation", Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing ICASSP 2006, Vol. 2, Toulouse, France, 2006, pp. 729-732.
7 Baataoui, A., Batteoui, I. E., Saaidi, A., and Satori, K., "Camera Self-calibration by an Equilateral Triangle", International Journal of Computer Application, Special Issue on Software Engineering, Databases and Expert Systems-SEDEX, 2012, pp. 29-34
8 Abdel-Aziz, Y. I., Karara, H. M., and Hauck, M., "Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry", Photogrammetric Engineering & Remote Sensing, Vol. 81, No. 2, 2015, pp. 103-107.   DOI
9 Liu, X., Su, H., Kang, S., Kane, T. D., and Shekhar, R., "Application of Single-Image Camera Calibration for Ultrasound Augmented Laparoscopic Visualization", Proceedings SPIE 9415, Medical Imaging 2015 : Image-Guided Procedures, Robotic Interventions, and Modeling, 94151T, Orlando, USA, 2015 : DOI: 10.1117/12.2082194.
10 Akkad, N. E., Merras, M., Saaidi, A., and Satori, K., "Robust method for self-calibration of cameras having the varying intrinsic parameters", Journal of Theoretical and Applied Information Technology, Vol. 50, No. 1, 2013, pp. 57-67.
11 Batz, M., Richter, T., Garbas, J. U., Papst, A., Seiler, J., and Kaup, A., "High dynamic range video reconstruction from a stereo camera setup", Signal Processing : Image Communication, Vol. 29, No. 2 2014, pp. 191-202.   DOI
12 Derpanis, K. G., Overview of the RANSAC Algorithm, Technical report Dept. of Computer Science, York University, 2010, pp. 1-2.
13 Erik, K., Usman, K., and Gunawan, I. P., "DCT-based local motion blur detection", Proceedings of International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering IEEE, Bandung, Indonesia, 2009, pp. 1-6.
14 Hemayed, E. E., "A survey of camera self-calibration", Proceedings of the IEEE Conference in Advanced Video and Signal Based Surveillance 2003, Miami, USA, 2003, pp. 351-357.
15 Farias, M. C., Mitra, S. K., and Foley, J. M., "Perceptual contributions of blocky, blurry and noisy artifacts to overall annoyance", Proceedings of the 2003 International Conference on Multimedia and Expo, Baltimore, USA, 2003, pp. I-529-I-532.
16 Fayyaz, R. and Rhee, E. J., "Hand Segmentation using Depth Information and Adaptive Threshold by Histogram Analysis with Color Clustering", Journal of Korea Multimedia Society, Vol. 17, No. 5, 2014, pp. 547-555.   DOI
17 Gonzales, R. C. and Woods, R. E., Digital Image Processing (3rd ed.), International edition, Pearson, 2008.