Author Affiliations
1Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technology and Physics, Chinese Academy of Sciences, Shanghai 200083, China2University of Chinese Academy of Sciences, Beijing 100049, Chinashow less
Fig. 1. Illustration of frequency domain segmentation for curvelet transformation
Fig. 2. Simulation results of weak texture signal images of submarine's V-wake in direct course. (a) Weak texture image in Ref. [7]; (b) weak texture image in Ref. [8]; (c) weak texture image in Ref. [15]; (d) weak texture image in Ref. [16]; (e) weak texture image in Ref. [7] with background; (f) weak texture image in Ref. [8] with background; (g) weak texture image in Ref. [15] with background; (h) weak texture image in Ref. [16] with background
Fig. 3. Contrast and average of grayscale of V-shape wake
Fig. 4. Directional component screening for V-shape wake. (a) Directional component sizer in frequency domain; (b) weak texture after directional component screening
Fig. 5. Optimized threshold screening for V-shaped wake
Fig. 6. Edge extraction of gradient operator for V-shaped wake
Fig. 7. Flowchart of algorithm
Fig. 8. Different V-shaped wakes and edge extraction results of different algorithms. (a) Model in Ref. [7] added with background; (b) result of proposed algorithm for Fig. 8(a); (c) result of curvelet transform for Fig. 8(a); (d) result of wavelet transform for Fig. 8(a); (e) model in Ref. [8] added with background; (f) result of proposed algorithm for Fig. 8(e); (g) result of curvelet transform for Fig. 8(e); (h) result of wavelet transform for Fig. 8(e); (i) model in Ref. [15] added with background; (j) result of proposed algorithm for Fig. 8(i); (k) result of curvelet transform for Fig. 8(i); (l) result of wavelet transform for Fig. 8(i); (m) model in Ref. [16] added with background; (n) result of proposed algorithm for Fig. 8(m); (o) result of curvelet transform for Fig. 8(m);(p) result of wavelet transform for Fig. 8(m)
Fig. 9. Algorithm adaptability of texture contrast
Fig. 10. Algorithm adaptability of a priori frequency screening direction
Algorithm | Entropy E | Frequencyconcentration F |
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Algorithm in the paper | 0.26 | 0.91 | Wavelet transformation | 0.19 | 0.39 | Curvelet transformation | 0.20 | 0.82 |
|
Table 1. Extraction results of different algorithms for V-shaped wake