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sift

于 2020-01-08 发布

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下载积分: 1 下载次数: 4

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说明： SIFT特征点的提取和匹配，SIFT算法首先在尺度空间上进行关键点检测，确定关键点的位置和关键点所处的尺度，然后使用关键点邻域梯度的主方向作为该点的方向特征，最后由关键点邻域梯度信息生成关键点的特征向量(SIFT algorithm firstly detects the key points in the scale space, determines the location of the key points and the scale of the key points, then uses the main direction of the key point neighborhood gradient as the direction feature of the point, and finally generates the feature vector of the key points from the key point neighborhood gradient information)

文件列表：

sift, 0 , 2020-01-08
sift\1.jpg, 203710 , 2018-12-14
sift\2.jpg, 69363 , 2005-06-10
sift\appendimages.m, 461 , 2005-07-08
sift\basmati.pgm, 46433 , 2005-07-08
sift\book.pgm, 97697 , 2005-07-08
sift\box.pgm, 72310 , 2005-07-08
sift\defs.h, 1920 , 2005-07-08
sift\LICENSE, 1055 , 2005-07-08
sift\Makefile, 631 , 2005-07-08
sift\match.c, 4876 , 2005-07-08
sift\README, 7952 , 2005-07-08
sift\scene.pgm, 196623 , 2005-07-08
sift\showkeys.m, 1699 , 2005-07-08
sift\sift, 45070 , 2005-07-08
sift\sift.m, 2432 , 2020-01-08
sift\siftWin32.exe, 94208 , 2005-07-08
sift\tmp.jpg, 90015 , 2020-01-08
sift\tmp.key, 469364 , 2020-01-08
sift\Untitled6.m, 116 , 2020-01-08
sift\util.c, 7493 , 2005-07-08

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2014-04-25 09:17:35下载

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本工程收集了多种常用条码的生成方法

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本文主要研究基于水平集方法的活动轮廓模型图像分割，在回顾活动轮廓模型发展的基础上，介绍了曲线演化理论及其水平集方法，证明了水平集方法可以有稳定的数值实现方式且其处理拓扑变化的自然性，进一步引出了变分水平集方法。本文介绍了两种不同的几何活动轮廓模型：基于梯度信息的李纯明模型以及基于区域信息的 Chan-Vese 模型（C-V 模型）。在分析上述两模型的优缺点上，提出了一种改进的 C-V 模型，改进模型引入了距离约束项，同时对基于区域的外部能量项进行了改进，使得水平集函数重新初始化寓于模型的演化之中，分割结果对参数的依赖性减小。 (This paper mainly study image segmentation of active contour model based on level set. After reviewing the development of the active contour model, curve evolution and level set method are presented, then we proved that the level set method has a stable numerical realization and can describe the topology change of the contour naturally, in addition we introduce level set of variational method. We introduce two different GAC models in this paper: Li Chunming model which is based on gradient information and Chan-Vese model （C-V model） which is based on region information. Based on the drawbacks and advantages of these two models, we improve the C-V model by adding penalizing energy term of signed distance function, at the same time, mending the external energy term which based on region information, then the re-initialization of the level set can accomplish in the model evolution, and the degree of the segmentation result depend on the parameters decline. )

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积分：1
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积分：1
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