Fast electronic digital image stabilization

Morimoto, Carlos H.; Chellappa, Rama

doi:10.1109/icpr.1996.546956

Cited by 106 publications

(61 citation statements)

References 6 publications

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“…It consists in analyzing the main displacement between two consecutive frames of the video, so called global motion or camera motion, in order to separate the intentional motion from the unwanted one. This last is then compensated, resulting in a video without jolts [1]. This is the stabilization scheme we have adopted.…”

Section: Introductionmentioning

confidence: 99%

An Image Sensor with Global Motion Estimation for Micro Camera Module

Gensolen

Cathébras

Martin

et al. 2005

Advanced Concepts for Intelligent Vision Systems

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Abstract. We describe in this paper the building of a vision sensor able to provide video capture and the associated global motion between two consecutive frames. Our objective is to propose embedded solutions for mobile applications. The global motion considered here is the one typically produced by handheld devices movement, which is required for our purpose of video stabilization. We extract this global motion from local motion measures at the periphery of the image acquisition area. Thanks to this peculiar and "taskoriented" configuration, the resulting system architecture can take advantage of CMOS focal plane processing capabilities without sacrificing the sensor fill factor. Our approach is currently implemented in a CMOS 0.13µm technology.

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Section: Introductionmentioning

confidence: 99%

An Image Sensor with Global Motion Estimation for Micro Camera Module

Gensolen

Cathébras

Martin

et al. 2005

Advanced Concepts for Intelligent Vision Systems

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“…In order to achieve fast and robust motion estimation of image sequences, the key is to distinguish the useful motion vectors and incorrect motion vectors, and minimize the amount of calculation. With a single two-dimensional rigid body motion model to estimate the motion of image sequences, significant motion estimation errors will occur when view depth changes largely [5]. So, the proposed 2.5-dimensional motion model is better than the two-dimensional affine model [6], and the method of motion vectors segmentation was used in [7].…”

Section: Introductionmentioning

confidence: 99%

Model Parameters Estimation for Image Motion Compensation

Zhong¹,

Li²,

Zhao³

2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

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Abstract-Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

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“…Motion estimation: Classic methods [20,45,46,60] either use correlation techniques or track salient points such as Harris corners [21] using, e.g., a KLT tracker [42,51]. Pyramidal structures are typically used for estimating large displacements [43,18,48].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Motion Smoothing Strategies for Video Stabilization using Parametric Models

Sánchez¹

2017

Image Processing on Line

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This paper is devoted to a rigorous implementation and to an exhaustive comparison of video stabilization techniques. These techniques aim at removing the undesirable effects of camera shake. They first estimate a global transform from frame to frame, which can be a translation, a similarity, an affine map or a homography. This generates a signal that can be smoothed and used to compensate the noisy transform signal. This paper compares all classic smoothing methods and their boundary conditions. It also analyzes two algorithms to crop the video after stabilization. The stabilization results are displayed in a scale-space form permitting to extract valuable information about ego-motion such as its frequencies and its general tendencies. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

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Fast electronic digital image stabilization

Cited by 106 publications

References 6 publications

An Image Sensor with Global Motion Estimation for Micro Camera Module

An Image Sensor with Global Motion Estimation for Micro Camera Module

Model Parameters Estimation for Image Motion Compensation

Comparison of Motion Smoothing Strategies for Video Stabilization using Parametric Models

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