Laser & Optoelectronics Progress, Volume. 56, Issue 3, 031010(2019)
Obstacle Recognition in Vegetation Environment Based on Markov Random Field
Fig. 1. Connections between adjacent points. (a) Simplest connection layout; (b) extension of distance between connected points
Fig. 3. Expansion of connections. (a) Point cloud; (b) untypical connections; (c) connections after expansion
Fig. 9. Results of different values of δ. (a) δ=0.4; (b) δ=0.6; (c) δ=0.8; (d) δ=1.0
Fig. 11. Detection results in artificial scene. (a) Manual marking; (b) proposed algorithm; (c) model matching algorithm; (d) covariance algorithm
Fig. 12. Detection results in field scene. (a) Manual marking; (b) proposed algorithm; (c) model matching algorithm; (d) covariance algorithm
Fig. 13. Comparison of running time of different algorithms. (a) Artificial scene; (b) field scene
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Ziyang Cheng, Guoquan Ren, Yin Zhang. Obstacle Recognition in Vegetation Environment Based on Markov Random Field[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031010
Category: Image Processing
Received: Jul. 30, 2018
Accepted: Aug. 31, 2018
Published Online: Jul. 31, 2019
The Author Email: Guoquan Ren (rrrgggqqq@163.com)