DOI QR코드

DOI QR Code

Performance Improvement of the SVM by Improving Accuracy of Estimating Vanishing Points

소실점 추정 정확도 개선을 통한 SVM 성능 향상

  • Received : 2016.09.19
  • Accepted : 2016.11.08
  • Published : 2016.12.31

Abstract

In this paper, we propose an improved single view metrology (SVM) algorithm to accurately measure the height of objects. In order to accurately measure the size of objects, vanishing points have to be correctly estimated. There are two methods to estimate vanishing points. First, the user has to choose some horizontal and vertical lines in real world. Then, the user finds the cross points of the lines. Second, the user can obtain the vanishing points by using software algorithm such as [6-9]. In the former method, the user has to choose the lines manually to obtain accurate vanishing points. On the other hand, the latter method uses software algorithm to automatically obtain vanishing points. In this paper, we apply image resizing and edge sharpening as a pre-processing to the algorithm in order to improve performance. The estimated vanishing points algorithm create four vanishing point candidates: two points are horizontal candidates and the other two points are vertical candidates. However, a common image has two horizontal vanishing points and one vertical vanishing point. Thus, we eliminate a vertical vanishing point candidate by analyzing the histogram of angle distribution of vanishing point candidates. Experimental results show that the proposed algorithm outperforms conventional methods, [6] and [7]. In addition, the algorithm obtains similar performance with manual method with less than 5% of the measurement error.

Keywords

References

  1. Y.M. Mustafah, A.W. Azman, M.H. Ani, "Object distance and size measurement using stereo vision system," Advanced Materials Research, Vol. 622-623, pp. 1373, 2013.
  2. C. Lu, X. Wang, Y. Shen, "A stereo vision measurement system based on OpenCV," Proceedings of IEEE International Congress on Image and Signal Processing, Vol. 2, pp. 718-722, 2013.
  3. A. Yamashita, T. Kaneko, S. Matsushita, K.T. Miura, S. Isogai, "Camera calibration and 3-D measurement with an active stereo vision system for handling moving objects," Journal of Robotics and Mechatronics, Vol. 15, No. 3, pp. 304-313, 2003. https://doi.org/10.20965/jrm.2003.p0304
  4. A. Criminisi, I. Reid, A. Zisserman, "Single view metrology," IEEE International Journal of Computer Vision, Vol. 40, No. 2, pp. 123-148, 2000. https://doi.org/10.1023/A:1026598000963
  5. K. Peng, L. Hou, R. Ren, X. Ying, H. Zha, "Single view metrology along orthogonal directions," Proceedings of IEEE International Conference on Pattern Recognition, Vol. 3, pp. 1658-1661, 2010.
  6. B. Li, K. Peng, X. Ying, H. Zha, "Simultaneous vanishing point detection and camera calibration from single images," Lecture Notes in Computer Science, Vol. 6454, pp. 151-160, 2010.
  7. J. Kosecka, W. Zhang, "Video compass," Proceedings of European Conference on Computer Vision, pp. 657-673, 2002 .
  8. K.S. Seo, C.J. Seo, H.M. Choi, "Log-polar coordinate image space for the efficient detection of vanishing points," ETRI Journal, Vol. 28, No. 6, pp. 819-821, 2006. https://doi.org/10.4218/etrij.06.0206.0086
  9. J.P. Tardif, "Non-iterative approach for fast and accurate vanishing point detection," Proceedings of IEEE International Conference on Computer Vision, pp. 1250-1257, 2009.
  10. W. McIlhagga, "The canny edge detector revisited," International journal of computer vision, Vol. 91, No. 3, pp. 251-261, 2011. https://doi.org/10.1007/s11263-010-0392-0
  11. W. Gao, L. Yang, X. Zhang, H. Liu, "An improved sobel edge detection," International Conference on Computer Science and Information Technology, pp. 67-71, 2010.