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	Incremental Translation Averaging 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 11, 页码: 7783-7795 作者: Gao, Xiang; Zhu, Lingjie; Fan, Bin; Liu, Hongmin; Shen, Shuhan \| 收藏 \| 浏览/下载：43/0 \| 提交时间：2022/12/27 Cameras Estimation Robustness Pipelines Cost function Parameter estimation Barium Translation averaging incremental estimation accuracy and robustness simplicity and efficiency
	Incremental Rotation Averaging 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 页码: 15 作者: Gao, Xiang; Zhu, Lingjie; Xie, Zexiao; Liu, Hongmin; Shen, Shuhan \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2021/03/01 Rotation averaging Incremental estimation Accuracy and robustness
	Study on image real-time interpretation based on particle filter (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, August 19, 2011 - August 22, 2011, Jilin, China 作者: Liu S.-J. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 In order to satisfy to the real-time requirement of image interpretation system in photoelectric measurement equipments a kind of hardware acceleration system with MIMD distributed multi-processor architecture based on SOPC technology is designed. The particle filter algorithm is proposed to process image interpretation for state estimation problem of nonlinear and non-Gaussian system. This algorithm does not involve conventional linearization transform and has approximated the posterior probability density by a set of discrete particles. Therefore the approximate optimum result is educed. It has a high accuracy and a rapid convergence. Experimental results show that the algorithm be adequate to real time accuracy and robustness meets the requirement of image interpretation and possesses practical significance for engineering applications. 2011 IEEE.
	A new accurate and fast algorithm of sub-pixel image registration (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE 10th International Conference on Signal Processing, ICSP2010, October 24, 2010 - October 28, 2010, Beijing, China 作者: He B. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 In terms of large field view and multi-channel TDICCD remote sensing images previous interpolation methods and curve fitting methods are not able to achieve the high accuracy and fast registration and their noise immunity and robustness is not high. For that this paper presents a more accurate and faster method iterative pixel interpolation and surface fitting method. Firstly the method uses the some overlapping pixels between multi-channel images. Secondly the proposed technique which is based on the maximization of the correlation coefficient function combines an efficient pixel-moving interpolation scheme with surface fitting which makes use of accurate interpolation calculation and fast surface fitting in the iterative process. Finally the accuracy and speed of the algorithm is evaluated by sub-pixel registration of multi-channel images and comparison with other sorts of efficient methods. The experiment results show that the accuracy of the method reaches 0.01 pixels and it is 3 times faster than the interpolation method. In the registration of large field view and multi-channel TDICCD images the method is accurate and fast with greatly high stability noise immunity and robustness. 2010 IEEE.
	A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 In many computer vision tasks in order to improve the accuracy and robustness to the noise wavelet analysis is preferred for the natural multi-resolution property. However the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour the Zernike moments are introduced and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient precise and robust.