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融合几何特征的压缩感知SIFT描述子  [PDF]
赵爱罡,王宏力,杨小冈,陆敬辉,何星
红外与激光工程 , 2015,
Abstract: 为了解决尺度不变特征变换(SIFT)描述子在存在较多相似结构的匹配中,易造成误匹配,并且维数较高、匹配耗时的问题,提出了一种融合相对几何位置的压缩感知描述子.首先,以特征点为中心,将周围关键点的相对几何位置(RGL)信息形成尺度和旋转不变的RGL描述子,其次,对SIFT描述子利用压缩感知(CS)理论进行降维,形成CS-SIFT描述子,最后将两者融合形成RGL-CS-SIFT描述子.实验结果表明:与SIFT和PCA-SIFT描述子相比,匹配速度有所提升,匹准确率明显提高.
A Comparison of SIFT, PCA-SIFT and SURF
Luo Juan,Oubong Gwun
International Journal of Image Processing , 2009,
Abstract: This paper summarizes the three robust feature detection methods: ScaleInvariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFTand Speeded Up Robust Features (SURF). This paper uses KNN (K-NearestNeighbor) and Random Sample Consensus (RANSAC) to the three methods inorder to analyze the results of the methods’ application in recognition. KNN isused to find the matches, and RANSAC to reject inconsistent matches fromwhich the inliers can take as correct matches. The performance of the robustfeature detection methods are compared for scale changes, rotation, blur,illumination changes and affine transformations. All the experiments userepeatability measurement and the number of correct matches for the evaluationmeasurements. SIFT presents its stability in most situations although it’s slow.SURF is the fastest one with good performance as the same as SIFT. PCA-SIFTshow its advantages in rotation and illumination changes.
SIFT applied to CBIR  [PDF]
ALMEIDA, J.,TORRES, R. S.,GOLDENSTEINS, S. K.
Salesian Journal on Information Systems , 2009,
Abstract: Content-Based Image Retrieval (CBIR) is a challenging task. Common approaches use only low-level features. Notwithstanding, such CBIR solutions fail on capturing some local features representing the details and nuances of scenes. Many techniques in image processing and computer vision can capture these scene semantics. Among them, the Scale Invariant Features Transform~(SIFT) has been widely used in a lot of applications. This approach relies on the choice of several parameters which directly impact its effectiveness when applied to retrieve images. In this paper, we discuss the results obtained in several experiments proposed to evaluate the application of the SIFT in CBIR tasks.
国外常见食品—匹查饼  [PDF]
张瑞霖
食品科学 , 1980,
Abstract: ?<正>匹查饼(pizza)是一种有托无盖的馅饼,匹查讲起源于意大利南部世界第三大港—那波里港。这种食品是那波里人民多年相传的传统食品,在第二次世界大战中美国兵进驻意大利
遥爪聚异丁烯的合成  [PDF]
吴一弦,张文芝,武冠英,李立新
化工学报 , 1992,
Abstract: 1引言遥爪聚异丁烯的制备生要有2种方法,即聚合物降解法和链引发转移剂法(Inifer)。通过Inifer技术,可以得到末端带有叔氯的遥爪聚异丁烯’“,再由氯端基经有机反应转化为带有不同官能团的遥爪聚异丁烯,如C一C—[2]、羟基[3,4]、浚基”‘和苯基”’等。Kennedy等“”‘研究表明,采用双端或三端引发剂,可得官能度凡。2或3土0.二的遥爪聚异丁烯。武冠英等”‘研究探讨了合成端氯基遥爪聚异丁烯过程中的环化反应和链转移反应。本文选用双端直链烷烃叔酯为引发剂,三氯化硼为共引发剂,于CHzCI。中合成线性叔氯遥爪聚异丁烯。
A COMPARISON OF SIFT AND SURF
P M PANCHAL,S R PANCHAL,S K SHAH
International Journal of Innovative Research in Computer and Communication Engineering , 2013,
Abstract: Accurate, robust and automatic image registration is critical task in many applications. To perform image registration/alignment, required steps are: Feature detection, Feature matching, derivation of transformation function based on corresponding features in images and reconstruction of images based on derived transformation function. Accuracy of registered image depends on accurate feature detection and matching. So these two intermediate steps are very important in many image applications: image registration, computer vision, image mosaic etc. This paper presents two different methods for scale and rotation invariant interest point/feature detector and descriptor: Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF). It also presents a way to extract distinctive invariant features from images that can be used to perform reliable matching between different views of an object/scene.
匹鲁卡品的电分析化学研究  [PDF]
汪乃兴,陈建民,陆瑞才,张晓岚,邓家祺
药学学报 , 1990,
Abstract: 本文用微分脉冲极谱法和循环伏安法研究了匹鲁卡品的电化学行为,选择了zn(ⅱ)—匹鲁卡品法测定的最佳条件,建立了测定方法,俭出限可达8.0×10-7mol/l;同时还制备了匹鲁卡品—pvc膜离子选择性电极,直接应用于匹鲁卡品注射液的测定,毋需分离,简便快速,结果良好。
Protection algorithm of face feature using SIFT
人脸特征的SIFT保护算法

Zhou Lingli,Lai Jianghuang,
周玲丽
,赖剑煌

中国图象图形学报 , 2011,
Abstract: With the growing use of face recognition in security and video control domain,there are growing concern about security and privacy of biometrics data.This paper proposes a security algorithm about face feature,which bases on the SIFT feature and random projection.Because the SIFT features are invariant to image rotation,scale and change in illumination,feature extraction is first performed on face images by SIFT algorithm.The SIFT features are transformed using invertible transformation which is generated b...
APPROACH TOWARDS CBIR USING SIFT  [PDF]
P.S.HANWATE,PROF. U.L.KULKARNI
International Journal of Engineering Science and Technology , 2012,
Abstract: Image retrieval is a main part of image processing. In browsing or searching image in the computer, retrieval concept is used. The best example is Google. In Google, we retrieve the images related to the word or input. In this case the problem is that an unwanted data or images are retrieved because the retrieval is based on word or any one of the basic method of image retrieval. This problem is overcome by the Content-Based Image Retrieval (CBIR). Content-Based Image Retrieval is a part of image processing and it also comes under artificial intelligence. We know that interest in digital images is growing day by day. Users in many professional fields are exploitingthe opportunities offered by the ability to access and manipulate remotely stored images in all kinds of new and exciting ways. The problems in image retrieval are becoming widely recognized, and the search for their solutions is going in an increasingly active area for research and development. Images in query produce the good result.In this paper , the CBIR uses video ( collection of image) as input using SIFT algorithms and Edge detection algorithm. Our proposed system will discuss the efficient result using SIFT and Edge detection algorithm. At first, we retrieves image by using any one of the basic method, after that we use the SIFT algorithm and edgedetection method. Then it will produce the key point descriptor (using distinct features of image).This key point descriptor are responsible for generating appropriate results.
边缘分类SIFT算法  [PDF]
付永庆,宋宝森,吴建芳
哈尔滨工程大学学报 , 2010,
Abstract: 针对经典SIFT算法的实时性较差和其在图像拼接应用时特征点冗余的问题,研究了其在整个尺度空间搜索极值点步骤后,利用图像几何不变矩理论结合图像边缘提取技术提取了图像的边缘类,并在边缘类对应的尺度空间中提取特征点,从而给出了一种改进的SIFT算法.经过多组实验后,结果显示改进后算法可以使图像的冗余特征点减少20%~50%,从而大大减少经典SIFT特征点冗余性并提高了算法的运行速度.
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