A Study on an Image Stabilization in Moving Vehicle
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In the image captured by the camera in a moving vehicle, there exist various motions due to the change of background, motion of objects in the image that make difficult to extract a pure vibrational motion by the camera. In this paper, we suggest an image stabilization with the elimination of various motion components based on the classification of motions in the image by their characteristics. After the elimination of various local motions, images are compensated and stabilized with the global motion caused by the camera. Also, we suggest an accurate and fast image stabilization by excluding regions of little information based on block differences and edge densities.Keywords:
Image stabilization
Motion field
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A new detection method for motion vector was proposed in order to stabilize the image sequence with rotation.Firstly,the block match algorithm is applied to multi-fields and the local translation vectors are gained.Then the global motion vector with rotary angle can be deduced according to the rigid motion model.The rigid motion model is more accurate mathematical description for the unstable image sequence than the translation motion model,so the proposed method has more steady effect.The image stabilization results for aerial sequence show that the peak signal noise ratio of new method has attained 29.47dB which is 2.02dB better than that of translation method.
Motion field
Sequence (biology)
Quarter-pixel motion
Image translation
SIGNAL (programming language)
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Optical Flow
Motion field
Representation
Structure from Motion
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This paper proposes a new digital image stabilization (DIS) system based on optical flow technique. Unlike previous DIS systems developed mainly for removing the translational motion disturbance, the proposed system removes not only the translational but also the rotational motion disturbances. A computational scheme that facilitates the local motion vector field to estimate the global disturbing translational and rotational motions is developed. First, the optical flow technique is used to estimate the local motion vector field of the image, yielding the velocity of each pixel in the current image frame. Then, the global translational and rotational motion parameters are determined in terms of the least squares estimation. Finally, these motion vectors are used to generate the counterbalance signals for removing the disturbance motion. Owing to the additional ability of the rotational motion removal, the new DIS system suppresses the undesirable translational and rotational disturbances effectively and thus enhances the DIS performance significantly.
Optical Flow
Motion field
Translational motion
Structure from Motion
Image stabilization
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Motion analysis
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The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements.< >
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Motion field
Position (finance)
Structure from Motion
Image plane
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As a fundamental technique in data-driven (or example-based) methods, motion blending has been employed to produce new motion clip from two or more clips or introduced as a medium to improve the final result of other method, for example, motion graphs. As methods of motion blending always require the system or users to classify the motion samples, it is difficult to accomplish motion blending automatically and to preserve local details, such as angular accelerations of joints of original motions. In this study, the authors developed a framework based on Laplacian coordinates to produce transitions between two motion clips, emphasising on preserving local details, without any need of motion classification. At first, multidimensional Laplacian coordinates are introduced to present local details of joints' angle. Then an error function is deduced to measure how much result motions differ from original ones in local details. By minimising such error function, new motions can then finally generated, which can preserve local details of input motion clips as much as possible. The following experiments show the effectiveness of the authors' framework.
Structure from Motion
Motion field
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Tracking (education)
Match moving
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Motion analysis
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Motion blur
Motion field
Quarter-pixel motion
Image translation
Structure from Motion
Linear motion
Motion analysis
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Motion field
Image plane
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When the aerial camera photograph,a variety of image motion is caused by prior to the flight, pitching, rolling and vibration and other reasons,thus leading to the existence of relative motion of the illuminated objects in the focal plane of a photosensitive medium, the image is blured,and the imaging quality of the camera is seriously affected. Various causes of image motion and effects on image is analyzed by this paper,the necessity of image motion compensation is expounded. By analyzing existed methods of image motion compensation ,and on this basis, a new multi degree of freedom motion compensation method is designed,through the parallel mechanism motion,for image motion compensation by optical image motion compensation principle,a variety of airborne camera to take pictures of the image motion also can be eliminated.
Motion field
Image plane
Quarter-pixel motion
Structure from Motion
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