Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Fundamental Matrix for Cameras with Radial Distortion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Highest accuracy fundamental matrix computation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Accurate hardware-based stereo vision
Computer Vision and Image Understanding
Gradient-based modified census transform for optical flow
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Overconstrained linear estimation of radial distortion and multi-view geometry
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This paper presents a method for detecting moving objects in two temporal succeeding images by calculating the fundamental matrix and the radial distortion and therefore, the distances from points to epipolar lines. In static scenes, these distances are a result of noise and/or the inaccuracy of the computed epipolar geometry and lens distortion. Hence, we are using these distances by applying an adaptive threshold to detect moving objects using views of a camera mounted on a Micro Unmanned Aerial Vehicle (UAV). Our approach uses a dense optical flow calculation and estimates the epipolar geometry and radial distortion. In addition, a dedicated approach of selecting point correspondences that suits dense optical flow computations and an optimization-algorithm that corrects the radial distortion parameter are introduced. Furthermore, the results on distorted ground truth datasets show a good accuracy which is outlined by the presentation of the performance on real-world scenes captured by an UAV.