Atmospheric turbulence mitigation using feature based optical flow

Author(s):  
Tufan Caliskan ◽  
Nafiz Arica
2007 ◽  
Author(s):  
Mikhail G. Danilouchkine ◽  
Frits Mastik ◽  
Antonius F. W. van der Steen
Keyword(s):  

Author(s):  
Ian Scott-Fleming ◽  
Keith Hege ◽  
David Clyde ◽  
Donald Fraser ◽  
Andrew Lambert

Author(s):  
R. Feng ◽  
X. Li ◽  
H. Shen

<p><strong>Abstract.</strong> Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To address this problem, we proposed an improved optical flow estimation concentrated on post-processing, namely displacement modification. First of all, the Laplacian of Gaussian (LoG) algorithm is employed to detect the abnormal value in color map of displacement. Then, the abnormal displacement is recalculated in the interpolation surface constructed by the rest accurate displacements. Following the successful coordinate transformation and resampling, the registration outcome is generated. Experiments demonstrated that the proposed method is insensitive in changeable region of mountainous remote sensing image, generating precise registration, outperforming the other local transformation model estimation methods in both visual judgment and quantitative evaluation.</p>


2020 ◽  
Vol 37 (12) ◽  
pp. 1958
Author(s):  
Wataru Suzuki ◽  
Atsushi Hiyama ◽  
Noritaka Ichinohe ◽  
Wakayo Yamashita ◽  
Takeharu Seno ◽  
...  

Robotica ◽  
2014 ◽  
Vol 34 (9) ◽  
pp. 1923-1947 ◽  
Author(s):  
Salam Dhou ◽  
Yuichi Motai

SUMMARYAn efficient method for tracking a target using a single Pan-Tilt-Zoom (PTZ) camera is proposed. The proposed Scale-Invariant Optical Flow (SIOF) method estimates the motion of the target and rotates the camera accordingly to keep the target at the center of the image. Also, SIOF estimates the scale of the target and changes the focal length relatively to adjust the Field of View (FoV) and keep the target appear in the same size in all captured frames. SIOF is a feature-based tracking method. Feature points used are extracted and tracked using Optical Flow (OF) and Scale-Invariant Feature Transform (SIFT). They are combined in groups and used to achieve robust tracking. The feature points in these groups are used within a twist model to recover the 3D free motion of the target. The merits of this proposed method are (i) building an efficient scale-invariant tracking method that tracks the target and keep it in the FoV of the camera with the same size, and (ii) using tracking with prediction and correction to speed up the PTZ control and achieve smooth camera control. Experimental results were performed on online video streams and validated the efficiency of the proposed method SIOF, comparing with OF, SIFT, and other tracking methods. The proposed SIOF has around 36% less average tracking error and around 70% less tracking overshoot than OF.


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