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	A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy 期刊论文 OAI收割 KNOWLEDGE-BASED SYSTEMS, 2020, 卷号: 189, 页码: 11 作者: Pan, Yiwei; Pan, Zhibin; Wang, Yikun; Wang, Wei \| 收藏 \| 浏览/下载：53/0 \| 提交时间：2020/03/30 Exact k-nearest neighbors Fast search algorithm Clustering Triangle inequality Optimal check strategy
	Adaptive step-size fast iterative shrinkage-thresholding algorithm and sparse-spike deconvolution 期刊论文 OAI收割 COMPUTERS & GEOSCIENCES, 2020, 卷号: 134, 页码: 12 作者: Pan, Shulin; Yan, Ke; Lan, Haiqiang; Badal, Jose; Qin, Ziyu \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2020/05/18 Fast iterative shrinkage-thresholding algorithm Adaptive step-size algorithm Linear search approach Convergence rate Sparse-spike deconvolution
	An auto-focus algorithm of fast search based on combining rough and fine adjustment (EI CONFERENCE) 会议论文 OAI收割 3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012, March 27, 2012 - March 29, 2012, Xiamen, China 作者: Zhang S.; Zhang Y. 收藏 \| 浏览/下载：32/0 \| 提交时间：2013/03/25 A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing and then the wavelet transform based image definition evaluation function which is sensitivity to the variation in image definition was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing. (2012) Trans Tech Publications.
	Layered fast correlation tracking algorithm combined with target feature (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China 作者: Guo L.-H. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 A new correlation tracking algorithm layered fast correlation tracking algorithm combined with target feature is proposed for target tracking in image sequences. Based on traditional correlation tracking algorithm according to resolution of real-time image the proposed algorithm chooses the designated layer image. At the same time the proposed algorithm uses a new search method combined with the target features to predict matching position which can improve the matching precision and reduce computational complexity. In addition the experimental results indicate that the proposed algorithm can overcome the influence of gray mutation and satisfy the requirement of real-time. 2009 SPIE.
	Displacement estimation by the phase-shiftings of fourier transform in present white noise (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wu Y.-H. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 Displacement estimation is a fundamental problem in Real-time video image processing. It can be typically approached by theories based on features in spatial domain. This paper presents an algorithm which improves the theory for estimating the moving object's displacement in spatial domain by its Fourier transform frequency spectrum. Because of the characters of Fourier transform the result is based on all the features in the image. Utilizing shift theorem of Fourier transform and auto-registration the algorithm employs the phase spectrum difference in polar coordinate of two frame images sequence with the moving target1 2. The method needn't transform frequency spectrum to spatial domain after calculation comparing with the traditional algorithm which has to search Direc peak and it reduces processing time. Since the technique proposed uses all the image information including all the white noise in the image especially and it's hard to overcome the aliasing from noises but the technique can be an effective way to analyze the result in little white noise by the different characters between high and low frequency bands. It can give the displacement of moving target within 1 pixel of accuracy. Experimental evidence of this performance is presented and the mathematical reasons behind these characteristics are explained in depth. It is proved that the algorithm is fast and simple and can be used in image tracking and video image processing.