Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
In order to quickly and efficiently get the information of the bottom of the shoe pattern and spraying trajectory, the paper proposes a method based on binocular stereo vision. After acquiring target image, edge detec...In order to quickly and efficiently get the information of the bottom of the shoe pattern and spraying trajectory, the paper proposes a method based on binocular stereo vision. After acquiring target image, edge detection based on the canny algorithm, the paper begins stereo matching based on area and characteristics of algorithm. To eliminate false matching points, the paper uses the principle of polar geometry in computer vision. For the purpose of gaining the 3D point cloud of spraying curve, the paper adopts the principle of binocular stereo vision 3D measurement, and then carries on cubic spline curve fitting. By HALCON image processing software programming, it proves the feasibility and effectiveness of the method展开更多
Bird flocking is a paradigmatic case of self‐organised collective behaviours in biology.Stereo camera systems are employed to observe flocks of starlings,jackdaws,and chimney swifts,mainly on a spot‐fixed basis.A po...Bird flocking is a paradigmatic case of self‐organised collective behaviours in biology.Stereo camera systems are employed to observe flocks of starlings,jackdaws,and chimney swifts,mainly on a spot‐fixed basis.A portable non‐fixed stereo vision‐based flocking observation system,namely FlockSeer,is developed by the authors for observing more species of bird flocks within field scenarios.The portable flocking observer,FlockSeer,responds to the challenges in extrinsic calibration,camera synchronisation and field movability compared to existing spot‐fixed observing systems.A measurement and sensor fusion approach is utilised for rapid calibration,and a light‐based synchronisation approach is used to simplify hardware configuration.FlockSeer has been implemented and tested across six cities in three provinces and has accomplished diverse flock‐tracking tasks,accumulating behavioural data of four species,including egrets,with up to 300 resolvable trajectories.The authors reconstructed the trajectories of a flock of egrets under disturbed conditions to verify the practicality and reliability.In addition,we analysed the accuracy of identifying nearest neighbours,and then examined the similarity between the trajectories and the Couzin model.Experimental results demonstrate that the developed flocking observing system is highly portable,more convenient and swift to deploy in wetland‐like or coast‐like fields.Its observation process is reliable and practical and can effectively support the study of understanding and modelling of bird flocking behaviours.展开更多
文摘Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
文摘In order to quickly and efficiently get the information of the bottom of the shoe pattern and spraying trajectory, the paper proposes a method based on binocular stereo vision. After acquiring target image, edge detection based on the canny algorithm, the paper begins stereo matching based on area and characteristics of algorithm. To eliminate false matching points, the paper uses the principle of polar geometry in computer vision. For the purpose of gaining the 3D point cloud of spraying curve, the paper adopts the principle of binocular stereo vision 3D measurement, and then carries on cubic spline curve fitting. By HALCON image processing software programming, it proves the feasibility and effectiveness of the method
基金National Natural Science Foundation of China,Grant/Award Number:62103451。
文摘Bird flocking is a paradigmatic case of self‐organised collective behaviours in biology.Stereo camera systems are employed to observe flocks of starlings,jackdaws,and chimney swifts,mainly on a spot‐fixed basis.A portable non‐fixed stereo vision‐based flocking observation system,namely FlockSeer,is developed by the authors for observing more species of bird flocks within field scenarios.The portable flocking observer,FlockSeer,responds to the challenges in extrinsic calibration,camera synchronisation and field movability compared to existing spot‐fixed observing systems.A measurement and sensor fusion approach is utilised for rapid calibration,and a light‐based synchronisation approach is used to simplify hardware configuration.FlockSeer has been implemented and tested across six cities in three provinces and has accomplished diverse flock‐tracking tasks,accumulating behavioural data of four species,including egrets,with up to 300 resolvable trajectories.The authors reconstructed the trajectories of a flock of egrets under disturbed conditions to verify the practicality and reliability.In addition,we analysed the accuracy of identifying nearest neighbours,and then examined the similarity between the trajectories and the Couzin model.Experimental results demonstrate that the developed flocking observing system is highly portable,more convenient and swift to deploy in wetland‐like or coast‐like fields.Its observation process is reliable and practical and can effectively support the study of understanding and modelling of bird flocking behaviours.
