Acta Optica Sinica, Volume. 38, Issue 2, 0215003(2018)
Line Tracking Method of Arm Vein Based on Bayesian Theory
Fig. 1. Determination of initial seed points. (a) NIR image; (b) image after Gabor filtering; (c) grid lines and candidate initial seed points; (d) effective initial seed points
Fig. 3. Partial initial seed points and their corresponding local vessel directions
Fig. 4. Three types of vessel structures. (a) Normal; (b) bifurcation; (c) crossing
Fig. 7. Tracking results. (a) NIR image; (b) image processed with Gabor filter; (c) tracked vessel edge points; (d) edge points of normal vessel; (e) edge points of bifurcation vessel; (f) edge points of crossing vessel
Fig. 8. Comparison of results of three methods. (a) NIR image 1; (b) NIR image 2; (c) image 1 processed with Gabor filter; (d) image 2 processed with Gabor filter; (e) image 1 processed with proposed method; (f) image 2 processed with proposed method; (g) image 1 processed with RLT; (h) image 2 processed with RLT; (i) image 1 processed with LAT; (j) image 2 processed with LAT
Fig. 9. ROC images extracted by three methods. (a) Proposed method; (b) RLT method; (c) LAT method
Fig. 10. Comparison of detection rate of three methods with different SNR. (a) Precision; (b) recall; (c) F-measure
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Haosheng Gao, Chaoying Tang, Xiaoteng Chen, Xiao Yu. Line Tracking Method of Arm Vein Based on Bayesian Theory[J]. Acta Optica Sinica, 2018, 38(2): 0215003
Category: Machine Vision
Received: Aug. 16, 2017
Accepted: --
Published Online: Aug. 30, 2018
The Author Email: Tang Chaoying (cytang@nuaa.edu.cn)