b站应援团怎么加入:图像拼接 SIFT资料合集
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Sift算法,我略知一二,无法仔细描述(刚也贴了2个最近的资料)。
人类视觉捕捉景物的时候,先粗略(rough),后细节(fine)的习惯,被研究图像视觉的采用。2点采样使用的情况,则整体图像被不断的1/2边长划分,不同的图像(矩阵)构成了不同分辨率的尺度空间(我们理解为不同层面的矩阵),尺度,Scale,这里就代表不同的空间比例。
reasonable assumptions the only possible scale-space kernel is theGaussian function. Therefore,
the scale space of an image is defined as a function, L(x; y;delta) that is produced from the convolution of a variable-scaleGaussian, G(x; y; delta), with an input image, I(x;y):
因此 ,一个图像的尺度空间,L(x,y,delta) ,定义为原始图像I(x,y)与一个可变尺度的2维高斯函数G(x,y,delta) 卷积运算。
Sift算法中,提到了尺度空间,请问什么是尺度和尺度空间呢?
而不同的L(x,y,delta)就构成了尺度空间( Space,我理解,由于描述图像的时候,一般用连续函数比较好描述公式,所以,采用空间集合 ,空间的概念正规一些),实际上,具体计算的时候,即使连续的高斯函数,都要被离散为(一般为奇数大小)(2*k+1)*(2*k+1)矩阵,来和数字图像进行卷积运算。
1、 SIFT 算法提出及其改进
SIFT算法由D.G.Lowe 1999年提出,2004年完善总结。代表性文献
[1]David G. Lowe, "Object recognition from localscale-invariant features," International Conference onComputer Vision, Corfu, Greece (September 1999),pp.1150-1157.
[2] David G. Lowe, "Distinctive image features fromscale-invariant keypoints," International Journal ofComputer Vision, 60, 2 (2004), pp. 91-110.
具体的MATLAB代码在
http://www.cs.ubc.ca/~lowe/keypoints/
Rob Hess 基于GSL和Opencv编写了C语言程序。具体的代码可以在
http://web.engr.oregonstate.edu/~hess/index.html中下载,可以在VC++.net环境中运行,在调试时要注意对GSL和Opencv的正确配置。
后来Y.Ke将其描述子部分用PCA代替直方图的方式,对其进行改进。
[3] Y. Ke and R. Sukthankar. PCA-SIFT: A MoreDistinctive Representation for Local ImageDescriptors.Computer Vision and Pattern Recognition, 2004
Yanke’s homepage:
http://www.andrew.cmu.edu/user/yke/
2、
3、 SIFT算法的主要特点:
a)SIFT特征是图像的局部特征,其对旋转、尺度缩放、亮度变化保持不变性,对视角变化、仿射变换、噪声也保持一定程度的稳定性。
b)独特性(Distinctiveness)好,信息量丰富,适用于在海量特征数据库中进行快速、准确的匹配[23]。
c) 多量性,即使少数的几个物体也可以产生大量SIFT特征向量。
d) 高速性,经优化的SIFT匹配算法甚至可以达到实时的要求。
e) 可扩展性,可以很方便的与其他形式的特征向量进行联合。
4、SIFT算法步骤:
1)检测尺度空间极值点
2)精确定位极值点
3)为每个关键点指定方向参数
4)关键点描述子的生成
SIFT算法的介绍参见:SIFT算法学习小记
、ubc:DAVID LOWE---SIFT算法的创始人,两篇巨经典经典的文章
http://www.cs.ubc.ca/~lowe/
2、cmu:YanKe---PCASIFT,总结的SIFT方面的文章SO全,巨经典
http://www.andrew.cmu.edu/user/yke/
3、ubc:MBROWN---SIFT算法用于图像拼接的经典应用autopano-sift,包括一个SIFTLIB库
http://www.cs.ubc.ca/~mbrown/autostitch/autostitch.html
http://www.cs.ubc.ca/~mbrown/panorama/panorama.html
4、toronto:Jepson---Matlab SIFT tutorial, 超级超级超级经典~
http://www.cs.toronto.edu/~jepson
5、ucla:Vedaldi---加州大学一个博士生编的SIFT,Matlab、C的都有,但没用过
http://www.cs.ucla.edu/~vedaldi/
6、一个小的拼接软件ptasmblr
http://www.tawbaware.com/ptasmblr.htm
几个关于sift的算法链接网址,以后要养成这种随时保存资料的好习惯!否则后面不知道又要花多少时间去找,最重要的是影响心情。
SIFT
比原有的harris点匹配方式具有各高的佩准准确度,只是算法速度较慢
SIFT
This
This
The
SIFT
Release history
- Version
4 (July 2005) - There
is now a Windows binary as well as one for Linux. The Matlab scripts have been improved and include code for matching keypoints between images. - Version
3 (August 2004) - This
fixes a bug in the displayed orientation of arrows drawn for each keypoint when using the "-display" option. This affects only arrow display and not the keypoint features themselves (thanks to Yannik Fournier, Tom Stepleton, and Rob Sim for identifying the bug). In addition, a Matlab program is now provided for running the binary and loading the keypoints into Matlab when running under Linux (thanks to D. Alvaro and J.J. Guerrero for the Matlab program). - Version
2 (September 2003) - Fixes
a bug in Version 1 of the sample code for doing matching (not the keypoints themselves) that incorrectly declared "unsigned char" as "char" (thanks to Yongqin Xiao and Suresh Lodha for their assistance in reporting this bug). This new version now finds more correct matches. The new version also contains more test data and raises the matching threshold. - Version
1 (June 2003) - Initial
demo release.
Related papers
The
- David
G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110. [PDF]
The
- David
G. Lowe, "Object recognition from local scale-invariant features," International Conference on Computer Vision, Corfu, Greece (September 1999), pp. 1150-1157. [PDF];
The
- David
G. Lowe, "Local feature view clustering for 3D object recognition," IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii (December 2001), pp. 682-688. [PDF];
Patents
- Method
and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image - David
G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application filed March 8, 1999. Asignee: The University of British