Rang Liu, Dejiang Wang, Ping Jia, Xin Che. Point Target Detection Based on Omnidirectional Morphology Filtering and Local Characteristic Criterion[J]. Acta Optica Sinica, 2017, 37(11): 1104001
- Acta Optica Sinica
- Vol. 37, Issue 11, 1104001 (2017)
Fig. 1. Morphologies of point targets with (a) one pixel, (b) two pixels, (c) three pixels, (d) four pixels, and (e) multiple pixels
Fig. 2. (a) Complex cloud background; (b) enlarged view of cloud background edge; (c) three-dimensional grey-scale map corresponding to Fig. 2(b)
Fig. 3. Structural elements in eight-directions. (a) 0° ; (b) 45°; (c) 90°; (d) 135°; (e) 180°; (f) 225°; (g) 270°; (h) 315°
Fig. 4. Candidate point configuration detected by 0°-direction structural element
Fig. 5. Comparison among detection results. (a) Original infrared image; (b) detection result by TH transformation; (c) detection result by omnidirectional morphology
Fig. 6. Sketch map of four directional vectors of candidate points
Fig. 7. Schematic of cross-pixel point target
Fig. 8. Gray level images of (a) point target imaging at pixel center, (b) point target across 4 pixels, and (c) noisy point
Fig. 9. Energy concentration degree of point targets
Fig. 10. Image acquisition equipment
Fig. 11. Target detection results. (a)-(c) Original infrared images; (d)-(f) results after adaptive threshold detection; (g)-(i) results after removal of background edges; (j)-(l) results after removal of noise
Fig. 12. Signal-to-noise ratio of point targets
Fig. 13. Processing results from different algorithms. (a) Max-median filter algorithm; (b) DoG scale-space detection algorithm; (c) BM3D algorithm; (d) GMM algorithm
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Table 1. Parameters of infrared focal plane detector
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Table 2. Performance comparison of target detection algorithms
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