Digital image translational and rotational motion stabilization using optical flow technique

Chang, Jyh‐Yeong; Hu, Wenfeng; Cheng, Mu-Huo; Chang, Bo-Sen

doi:10.1109/tce.2002.1010098

Cited by 122 publications

(8 citation statements)

References 12 publications

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“…Electonic image stabilization [1] is a new solution for the video sequences with blurs and vibrations caused by the fierce movement of cameras.Actually,it is a technic which determin the shifts and compensate the movements between frames by the digital image procession methods.It's been more than 30 years since the thoery was raised.Now,there are many algrithms for the electonic image stabilization.Most of them are able to deal with simple and gentle movements,but for the complex and fierce movements,for example,the combination of translation and rotation movements. Compared with the conventional ways such as block matching [2] ,representitive piont matching [3] and so on,the grey projection matching [4] and circle projection matching [5] is more accurate and have less computations.However,the grey projection matching can only deal with the video sequences with translation motions,but rotation motions.In most cases,the carriers of camera experience complex motions.In order to deal with the rotations motion,the circle projection matching performs well.The combination of the two methods can deal with the problems mentioned above.…”

Section: Introductionmentioning

confidence: 99%

An Electronic Image Stabilizition Algorithm Based on Gray Projection and Circle Projection Matching

Wang

Sun

Han

2014

AMM

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In most video systems,the carrier of camera expierences all kinds of motions which make the video ambiguous and unstable.Usually,the gray projection matching algorithm can only stabilize the translation motion between frames,but the rotation motion of frames in a video sequence.By zoning the image into few small regions,the rotation motion could be ignored,then, local movement vector is aqcuired by the grey projection matching method and compensate the movement between frames.Then for the rotation movement,it can be stabilized by the compensation with circle projection matching.

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Section: Introductionmentioning

confidence: 99%

An Electronic Image Stabilizition Algorithm Based on Gray Projection and Circle Projection Matching

Wang

Sun

Han

2014

AMM

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“…Using the instantaneous velocity vector information of each pixel to calculate the velocity field of the image sequence intensity change, the optical flow method [2,3,4]has the aperture and occlusion problem ,which brings the large amount of calculation, in addition, the optical flow constraint equation is not real-time tenable.…”

Section: Introductionmentioning

confidence: 99%

A Infrared Small Moving Object Extraction Method in the Context of Complex Background Motion

Zhang

Wang

2013

AMR

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The detection of small moving target in the context of complex background is a difficult issue. A method combining interframe differential registration and adaptive wiener filtering aimed to suppress background to detect moving object in complex background is proposed. The fixed background in the fore-and-aft frames can be filtered out by the interframe registration which preserves the moving target, parts of background and noise due to interframe movement and the gray-scale fluctuation. On one hand the complex background is estimated by an adaptive wiener filter, and the background suppression leaves the high-frequency regions containing the moving target in image. On the other hand, most of the high-frequency regions corresponding to non-target area are eliminated by the inter-frame registration in the differential images. The motion of target is continual in image sequences, while the position of the leaked background is relatively fixed and the noise is of small size. The fusion of the background suppression and inter-frame registration makes the discrimination of targets, background and noise possible. The small moving target is detected by trajectory association based on its interframe trajectory continuity. Experiment results verify the feasibility of the method.

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“…These schemes reduce the computation complexity, and reduce the motion estimation accuracy at the same time. Besides block matching method with full or partial pixel information, optical flow [8] based method has been proposed for image stabilization. In optical flow algorithm the constraints are the first-order approximation of Taylor series, which means that the motions can not be very large.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Points Extraction Based on Harris and SIFT

Guo¹,

Cui²,

Fang³

2013

AMM

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This article presents a novel approach to extract robust local feature points of video sequence in digital image stabilization system. Robust Harris-SIFT detector is proposed to select the most stable SIFT key points in the video sequence where image motion is happened due to vehicle or platform vibration. Experimental results show that the proposed scheme is robust to various transformations of video sequences, such as translation, rotation and scaling, as well as blurring. Compared with the current state-of-the-art schemes, the proposed scheme yields better performances.

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Digital image translational and rotational motion stabilization using optical flow technique

Cited by 122 publications

References 12 publications

An Electronic Image Stabilizition Algorithm Based on Gray Projection and Circle Projection Matching

An Electronic Image Stabilizition Algorithm Based on Gray Projection and Circle Projection Matching

A Infrared Small Moving Object Extraction Method in the Context of Complex Background Motion

Robust Feature Points Extraction Based on Harris and SIFT

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