Laser & Optoelectronics Progress, Volume. 57, Issue 21, 210101(2020)
Shape Object Matching Recognition of Turbulence Clutter Based on Improved Shape Context
Fig. 1. Schematic of OSC description operator construction
Fig. 2. Feature histogram extracted by OSC descriptor. (a) Marking point 1; (b) marking point 2; (c) marking point 3
Fig. 3. Schematic of imposing edge continuity constraint
Fig. 4. Flow chart of algorithm implementation
Fig. 5. Simulation shape point sets under turbulence clutter scene. (a) Original image; (b) shape simultaneously degraded by rotation and deformation; (c) turbulence clutter degraded by deformation, outliers, and rotation; (d) strong turbulence clutter shapes degraded by deformation, outliers, and rotation
Fig. 6. Long distance imaging data. (a)(b) Image data in turbulent clutter scene; (c)(d) manual model shape point set of personal car and truck
Fig. 7. Average matching error and the experimental parameters
Fig. 8. Matching results of proposed method for deformable target ‘Fish’ in turbulent clutter scene. (a) Target matching results in both turbulent noise and rotating scene; (b) target matching results in turbulent noise scene
Fig. 10. Matching results of different methods for grid deformable target ‘personal car’ in actual outfield turbulence clutter scene. (a) Acquired image and enlarged target; (b) contour shape point set target obtained by method in Ref.[19]; (c) APM method [5]; (d) IDSC method[8]; (e) proposed method
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Xu Xinggui, Ran Bing, Yang Ping, Xian Hao, Liu Yong. Shape Object Matching Recognition of Turbulence Clutter Based on Improved Shape Context[J]. Laser & Optoelectronics Progress, 2020, 57(21): 210101
Category: Atmospheric Optics and Oceanic Optics
Received: Dec. 23, 2019
Accepted: --
Published Online: Oct. 24, 2020
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