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	Radar and Rain Gauge Merging-Based Precipitation Estimation via Geographical-Temporal Attention Continuous Conditional Random Field 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 卷号: 56, 期号: 9, 页码: 5558-5571 作者: Tang, Yongqiang; Yang, Xuebing; Zhang, Wensheng; Zhang, Guoping \| 收藏 \| 浏览/下载：44/0 \| 提交时间：2018/10/10 Continuous Conditional Random Field (Ccrf) Merging Method Precipitation Estimation Spatiotemporal Correlation
	A novel track initiation method for track splitting and merging 会议论文 OAI收割 OCEANS 2016 - Shanghai, Shanghai, China, April 10-13, 2016 作者: Li DD(李冬冬); Zhang Y(张瑶); Lin Y(林扬); Liu J(刘健) 收藏 \| 浏览/下载：35/0 \| 提交时间：2016/08/10 multiple target tracking track initiation splitting merging multiple hypotheses tracking modified logic-based method clutters
	Fast color-transfer-based image fusion method for merging infrared and visible images (EI CONFERENCE) 会议论文 OAI收割 Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, April 7, 2010 - April 8, 2010, Orlando, FL, United states 作者: Xu S. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 We present a computationally efficient color image fusion algorithm for merging infrared and visible images. At the core of the proposed method is the color transfer technique based on the linear YCBCR space. The method directly uses the grayscale fused image and the difference signals of the input images to construct the source YCBCR components then uses the statistical color transfer technique to form a color fused image that takes the target image's color characteristics. Two different strategies which respectively employ the pixel averaging fusion scheme and the multiresolution fusion scheme as the grayscale image fusion solution are proposed to fulfill different user needs. The simple strategy using the pixel averaging fusion scheme answers to a need of easy implementation and speed of use. And the complex strategy using the multiresolution fusion scheme answers to the high quality need of the fused products. In addition we also describe some useful theories about color-transfer-based image fusion. Experimental results show that the proposed color image fusion algorithm can effectively produce a natural appearing "daytime-like" color fused image and even using the pixel averaging fusion scheme to implement the grayscale fusion can also provide a pleasing result. 2010 Copyright SPIE - The International Society for Optical Engineering.
	A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Yao Z.-J. 收藏 \| 浏览/下载：30/0 \| 提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However 3. applying the standard Hough transform equation to every point of the input image edge 4. according to the local threshold 6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes traditional Hough transform can only detect the lines 2. quantizing the parameter space and extracting a group of maximums according to the global threshold eliminating spurious peaks which are caused by the spreading effects and will improve the precision of tracking. cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately 5. fixing on the endpoints of the segments according to the dynamic clustering rule which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image parameter and line-segment spaces