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Image registration plays a major role in many areas such as remote sensing, astronomy, biomedical imaging, and so on. Our main contribution in this paper is to present a new subpixel image registration that aligns translated of pair images. This algorithm combines well-known phase correlation technique with the differential methods of the optical flow field, especially the Locus-Kanade technique to...
the motion estimation problem in image sequences is one of the most important tasks in computer vision. Thus, many methods were proposed to resolve this problem, but no universal method has been developed to determine the motion in all the situations and for all the types of objects in motion. In this paper, we propose to combine the advantages of neural methods in particular EMAN method and the frequency...
This paper deals with design of hybrid software and hardware sensor, which can be used for mobile robots visual odometry task. The paper is focused to sensing raw data only. This approach combines onboard hardware sensors - accelerometer, gyroscope, magnetometer and camera with software module, which is mainly based on computer vision. Output from vision system is relative change of position and rotation...
We propose a fast and robust 2D-affine global motion estimation algorithm based on phase-correlation in the Fourier-Mellin domain and robust least square model fitting of sparse motion vector field and its application for digital image stabilization. Rotation-scale-translation (RST) approximation of affine parameters is obtained at the coarsest level of the image pyramid, thus ensuring convergence...
Correlation of images is a common step in image registration algorithms. Therefore it is important that the correlation step be efficient and accurate. This paper introduces the ‘shear average’ method for computing the relative translation between two similar images. We demonstrate that the proposed technique performance is comparable to traditional phase-correlation based techniques, while also being...
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