A NOVEL GRID INTERSECTION POINT DETECTION AND MATCHING METHOD IN THE BINOCULAR PULSE MEASUREMENT SYSTEM

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

8
Reader(s)
17
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 9 , ISSUE 1 (March 2016) > List of articles

A NOVEL GRID INTERSECTION POINT DETECTION AND MATCHING METHOD IN THE BINOCULAR PULSE MEASUREMENT SYSTEM

L. M. Yang / A. H. Zhang / D. M. Lin / L. Zhu

Keywords : Grid image, intersection point detection, image segmentation, ridge line fitting, global feature, cluster analysis.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 1, Pages 256-273, DOI: https://doi.org/10.21307/ijssis-2017-869

License : (CC BY-NC-ND 4.0)

Received Date : 17-November-2015 / Accepted: 22-January-2016 / Published Online: 01-March-2016

ARTICLE

ABSTRACT

To improve the accuracy of binocular 3D image reconstruction, the grid-pattern structure lines are printed on the detected objects and the grid lines intersection points are adopted as feature points and primitives in matching process. In this paper, a novel method for detecting the intersection points of the grid lines based on image segmentation and ridge line fitting is proposed. Firstly, the set of line segments on the border of the grid lines are extracted using the Canny edge detector and Hough transformation. Then, the global structure parameters are acquired through cluster analysis. Secondly, the grayscale image is divided into several detection regions (each of which includes one intersection point to be detected) in accordance to the obtained global structure parameters and the intersection points in the detection regions are accurately located using the ridge line fitting method. Finally, the intersection points in the left and right images are matched based on their distributions. To examine the detection performance of the proposed method, experiments have been conducted on actual and polluted images, respectively. The experimental results have demonstrated that the recognition ratio of the intersection points by the proposed ridge line fitting-based method is as high as 100%, the false positive ratio is 0 and the matching accuracy is up to 100%. Compared with the results obtained using traditional methods, the proposed method detection results are characterized by high accuracy, stability, uniqueness and invariability. Hence, the proposed method can meet the demands of 3D image reconstruction.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] Z. F. Fei, “Contemporary Sphygmology in Traditional Chinese Medicine”, People's Medical Publishing House, 2003.
[2] Y. F. Chung, Y. W. Chu, C. Y. Chung, C. S. Hu and C. H. Luo, “New vision of the pulse conditions using bi-sensing pulse diagnosis instrument”. Proceedings of 2013 International Conference on Orange Technologies, pp. 5-8, 2013.
[3] K. Malinauskas, P. Palevicius, M. Ragulskis, V. Ostasevicius and R. Dauksevicius, “Validation of noninvasive MOEMS-Assisted measurement system based on CCD sensor for radial pulse analysis”, Sensors, vol. 13, no. 4, pp. 5368-5380, 2013.
[4] P. Wang, “Pulse information detection method based on vision measurement”, M.S. thesis, Lanzhou University of Technology, 2014.
[5] C. Gao, H. J. Zhu and Y. C. Gao, “Analysis and improvement of SUSAN algorithm”, Signal Processing, vol. 92, no. 10, pp. 2552-2559, 2013.
[6] A. Kovacs and T. Sziranyi, “Improved harris feature point set for orientation-sensitive urban-area detection in aerial images”, IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 796-800, 2013.
[7] J. C. Gao, M. Y. Liu, F. Xu, “Moving target detection based on global motion estimation in dynamic environment”, International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 3, pp. 360-379, 2014.
[8] F. Dellinger, J. Delon, Y. Gousseay, J. Michel and F. Tupin, “SAR-SIFT: A SIFT-Like algorithm for SAR images”, IEEE Transactions On Geoscience And Remote Sensing, vol. 53, pp. 453-466, 2015.
[9] T. K. Kang, I. H. Choi and M. T. Lim, “MDGHM-SURF: A robust local image descriptor based on modified discrete Gaussian–Hermite moment”, Pattern Recognition, vol. 48, no. 3, pp. 670-684, 2015.
[10] C. Fatichah, D. Purwitasari, V. Hariadi, and F. Effendy, “Overlapping white blood cell segmentation and counting on microscopic blood cell images”, International Journal on Smart Sensing and Intelligent Systems, vol. 7, no, 3, pp. 1271-1286, 2014.
[11] N. Batool and R. Chellappa, “Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints”, Pattern Recognition, vol. 48, no. 3, pp. 642-658, 2015.
[12] Y. Wang, C. Wang, “Computer Vision-based Color Image Segmentation With Improved Kernel Clustering”, International Journal on Smart Sensing and Intelligent Systems, vol 8, no 3, pp. 1706-1729, 2015.
[13] A. A. Micheal, K. Vani and S Sanjeevi, “Automatic detection of ridges in lunar images using phase symmetry and phase congruency”, Computers and Geosciences, vol. 73, pp. 122-131, 2014.
[14] D. A. Frost, S. Rost, N. D. Selby and G. W. Stuart, “Detection of a tall ridge at the core–mantle boundary from scattered PKP energy”, Geophysical Journal International, vol. 195, pp. 558-574, 2013.

EXTRA FILES

COMMENTS