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	A Cerebrovascular Image Segmentation Method Based on Geometrical Feature Point Clustering and Local Threshold 期刊论文 OAI收割 CURRENT MEDICAL IMAGING REVIEWS, 2018, 卷号: 14, 期号: 5, 页码: 748-770 作者: Liu, Bin; Zhu, Chen; Qu, Xiaofeng; Wang, Mingzhe; Zhang, Song \| 收藏 \| 浏览/下载：41/0 \| 提交时间：2019/12/10 Digital subtraction angiography cerebrovascular image segmentation feature point clustering local threshold SIFT algorithm DSA interventional therapy
	Real-time SIFT-based Object Recognition System 会议论文 OAI收割 Takamatsu, Japan, Aug, 4-7, 2013 作者: Wang, Zhao; Xiao, Han; He, Wenhao; Wen, Feng; Yuan, Kui \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2016/10/17 Object Recognition Sift Keypoints Embedded System K-d Tree Bbf Algorithm
	Image matching combine SIFT with regional SSDA (EI CONFERENCE) 会议论文 OAI收割 2012 International Conference on Control Engineering and Communication Technology, ICCECT 2012, December 7, 2012 - December 9, 2012, Shenyang, Liaoning, China 作者: Liu J.; Liu J.; Liu J.; Zhao J. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 Image matching is at the base of many computer vision problems such as object recognition or image stitching. Standard SIFT provides poor performance when images under viewpoint change conditions and with similar corners. Hence we propose a matching algorithm combine regional SSDA with simplified SIFT algorithm. We demonstrate through experiments that our algorithm yields better performance in images of viewpoint change and similar feature points. Besides the simplified algorithm cut down about half the time was originally needed in our tested images. 2012 IEEE.
	Features extraction and matching of teeth image based on the SIFT algorithm (EI CONFERENCE) 会议论文 OAI收割 2012 2nd International Conference on Computer Application and System Modeling, ICCASM 2012, July 27, 2012 - July 29, 2012, Shenyang, China 作者: Wang X.; Wang X.; Wang X. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 Using of SIFT algorithm in the image of teeth model can detect the features of the teeth image effectively. In this approach first search over all scales and image locations by using a difference-of-Gaussian function to identify potential interest points that are invariant to scale and orientation. Second select keypoints based on measures of their stability and a detailed model is fit to determine location and scale at each candidate location. Third assign one or more orientations to each keypoint location based on local image gradient directions. Last measure the local image gradients at the selected scale in the region around each keypoint. And then use the KNN algorithm to match the features. Through lots of experiments and comparing with other feature extraction methods this method can detect the features of the teeth model effectively and offer some available parameters for 3D reconstruction of the teeth model. the authors.
	Rotation and scaling invariant feature lines for image matching (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: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 Image matching has been one of the most fundamental issues computer vision over the decades. In this paper we propose a novel method based on making use of feature lines in order to achieve more robust image matching. The feature lines have the properties of rotation and scaling invariance coined RIFLT(Rotation invariant feature line transform). Experimental results demonstrate the effectiveness and efficiency of the proposed method. Compare with the famous powerful algorithm Scale Invariant Feature Transform(SIFT) the proposed method is more insensitive to noise. And for certain sequence of images which contain clear lines the proposed method is more efficiency. Using the feature lines obtained by our method it is possible to matching two scene images with different rotation angles scale and light distort. 2011 IEEE.
	Mean shift tracking combining SIFT (EI CONFERENCE) 会议论文 OAI收割 2008 9th International Conference on Signal Processing, ICSP 2008, October 26, 2008 - October 29, 2008, Beijing, China 作者: Xue C. 收藏 \| 浏览/下载：65/0 \| 提交时间：2013/03/25 A novel visual tracking algorithm to cope with occlusion and scale variation is proposed. This method combines mean shift and SIFT algorithm to track object. SIFT algorithm is invariant to rotation translation and scale variation. But it is a timeconsuming algorithm. The wasting time is related to image size. So the proposed algorithm first adopts mean shift to initially locate object position then SIFT operator is used to detect features in object area and model area lastly the proposed method matches features in these two areas and calculates the relationship between them using affine transform. According to affine transform parameters the state of object can be adjusted in time. In order to reduce process time an improved feature matching algorithm is proposed in this paper. Experiments show that the proposed algorithm deals with occlusion successfully and can adjust object size in time. 2008 IEEE.