SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过...SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过程中加入了渐变的信息。实验结果表明,提出的G-SURF(Gradual-SURF)算子可以获得更稳定的效果,并且同时计算复杂度也有所改善。展开更多
Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration whe...Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.展开更多
文摘SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过程中加入了渐变的信息。实验结果表明,提出的G-SURF(Gradual-SURF)算子可以获得更稳定的效果,并且同时计算复杂度也有所改善。
基金supported by the National Natural Science Foundation of China (60802043)the National Basic Research Program of China(973 Program) (2010CB327900)
文摘Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.
