Calibration-free rolling shutter removal

Grundmann, Matthias; Kwatra, Vivek; Castro, Daniel Coelho de; Essa, Irfan

doi:10.1109/iccphot.2012.6215213

Cited by 161 publications

(127 citation statements)

References 10 publications

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“…In the case of rolling shutter cameras, the pointer bends in the images because of different exposure synchronization within an image frame (see Figure 16). The use of such cameras would require a rolling shutter correction (e.g., Grundmann et al, 2012;Ito and Okatani, 2016). However, even with such a correction, it is expected that the accuracy is not equal to global shutter cameras.…”

Section: Discussionmentioning

confidence: 99%

Wired and Wireless Camera Triggering With Arduino

Kauhanen

Rönnholm

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Synchronous triggering is an important task that allows simultaneous data capture from multiple cameras. Accurate synchronization enables 3D measurements of moving objects or from a moving platform. In this paper, we describe one wired and four wireless variations of Arduino-based low-cost remote trigger systems designed to provide a synchronous trigger signal for industrial cameras. Our wireless systems utilize 315MHz or 434MHz frequencies with noise filtering capacitors. In order to validate the synchronization accuracy, we developed a prototype of a rotating trigger detection system (named RoTriDeS). This system is suitable to detect the triggering accuracy of global shutter cameras. As a result, the wired system indicated an 8.91 μs mean triggering time difference between two cameras. Corresponding mean values for the four wireless triggering systems varied between 7.92 and 9.42 μs. Presented values include both camera-based and trigger-based desynchronization. Arduino-based triggering systems appeared to be feasible, and they have the potential to be extended to more complicated triggering systems.

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Section: Discussionmentioning

confidence: 99%

Wired and Wireless Camera Triggering With Arduino

Kauhanen

Rönnholm

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Baker et al [2] pose the rectification as a superresolution problem that can be solved using optical flow. Also Grundmann et al [10] exploit local flow vectors to compensate rolling shutter for uncalibrated cameras, but using a mixture of homographies. In contrast, Hanning et al [12] and Karpenko et al [1] use gyroscopes of cell phones to compensate for rotational shake.…”

Section: Previous Workmentioning

confidence: 99%

Rolling Shutter Stereo

Saurer

Köser

Bouguet

et al. 2013

2013 IEEE International Conference on Computer Vision

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A huge fraction of cameras used nowadays is based on CMOS sensors with a rolling shutter that exposes the image line by line. For dynamic scenes/cameras this introduces undesired effects like stretch, shear and wobble. It has been shown earlier that rotational shake induced rolling shutter effects in hand-held cell phone capture can be compensated based on an estimate of the camera rotation. In contrast, we analyse the case of significant camera motion, e.g. where a bypassing streetlevel capture vehicle uses a rolling shutter camera in a 3D reconstruction framework. The introduced error is depth dependent and cannot be compensated based on camera motion/rotation alone, invalidating also rectification for stereo camera systems. On top, significant lens distortion as often present in wide angle cameras intertwines with rolling shutter effects as it changes the time at which a certain 3D point is seen. We show that naive 3D reconstructions (assuming global shutter) will deliver biased geometry already for very mild assumptions on vehicle speed and resolution. We then develop rolling shutter dense multiview stereo algorithms that solve for time of exposure and depth at the same time, even in the presence of lens distortion and perform an evaluation on ground truth laser scan models as well as on real street-level data.

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“…To remove the artifacts, methods such as [19], [20] estimate the parametric motion between consecutive frames and apply the regularization method to minimize temporal derivative of the motion. Recently, Grundmann et al [21] introduced a calibration-free algorithm, which removes blind rolling shutters using mixture model of homographies, to handle challenging artifacts introduced by various CMOS cameras.…”

Section: Related Workmentioning

confidence: 99%

Spatially and Temporally Optimized Video Stabilization

Wang

Liu

Hsu

et al. 2013

IEEE Trans. Visual. Comput. Graphics

101

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Abstract-Properly handling parallax is important for video stabilization. Existing methods that achieve the aim require either 3D reconstruction or long feature trajectories to enforce the subspace or epipolar geometry constraints. In this paper, we present a robust and efficient technique that works on general videos. It achieves high-quality camera motion on videos where 3D reconstruction is difficult or long feature trajectories are not available. We represent each trajectory as a Bé zier curve and maintain the spatial relations between trajectories by preserving the original offsets of neighboring curves. Our technique formulates stabilization as a spatialtemporal optimization problem that finds smooth feature trajectories and avoids visual distortion. The Bé zier representation enables strong smoothness of each feature trajectory and reduces the number of variables in the optimization problem. We also stabilize videos in a streaming fashion to achieve scalability. The experiments show that our technique achieves high-quality camera motion on a variety of challenging videos that are difficult for existing methods.

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Calibration-free rolling shutter removal

Cited by 161 publications

References 10 publications

Wired and Wireless Camera Triggering With Arduino

Wired and Wireless Camera Triggering With Arduino

Rolling Shutter Stereo

Spatially and Temporally Optimized Video Stabilization

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