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SIFT,SURF,ORB,FAST,BRISK 特征提取算法比较

SIFT,SURF,ORB,FAST 特征提取算法比较

图像处理的基础就是要进行特征点的提取，feature(interest points) detect 的方法也在不断的进步，边检测，角点检测，直线检测，圆检测，SIFT特征点检测，同时描述符也在发展，为了匹配的高效，逐渐从高维特征向量到二进制向量…下面做一个简单的罗列，并调用opencv API看看效果！
承接上一篇文章。

Feature Detection Methods List:

  • Canny Edge Detect, A Computational Approach to Edge Detection, 1986. The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images.
  • Harris, A combined corner and edge detector, 1988. considering the differential of the corner score with respect to direction directly.
  • GFTT，Good Features to Track,1994, Determines strong corners on an image.
  • Matas-2000, Robust Detection of Lines Using the Progressive Probabilistic Hough Transform. 霍夫变换检测直线.
  • SIFT,Distinctive Image Features from Scale-Invariant Keypoints,2004, invariant to image translation, scaling, and rotation, partially invariant to illumination changes and robust to local geometric distortion. 128-dim(512B).
  • SURF, Speeded Up Robust Features,2006,受SIFT启发，比SIFT快，健壮. 64-dim(256B).
  • FAST ,Machine Learning for High-speed Corner Detection, 2006,wiki. Very fast, not robust to high level noise.
  • ORB, ORB: an efficient alternative to SIFT or SURF,2011，基于FAST和BRIEF，比SIFT快两个数量级，可作为SIFT的替代(a fusion of FAST keypoint detector and BRIEF descriptor). 32B binary descriptor.
  • BRISK，BRISK: Binary Robust Invariant Scalable Keypoints, 2011 . 64B binary descriptor.
  • STAR，Censure: Center surround extremas for realtime feature detection and matching,2008,引用次数不高.scale-invariant center-surround detector (CENSURE) that claims to outperform other detectors and is capable of real-time implementation.
  • MSER，Robust Wide Baseline Stereo from Maximally Stable Extremal Regions, 2002, 斑点检测(blob detection).

特征点提取算法比较(image dataset(27 pictures)):

ImageNOSIFTSURFORBFASTSTARBRISK
024144126500119787151538
1429581295001676311661861
234044784500161918161445
3163928025007166203699
4151014844972956223833421
5105728309500720065
6191187295161258251782
7335247065005671543
81654033742670115582762
948997523500127804731299
1019794212500106768641498
1135993294500663070
121631682877923661953
1318842413500116815482683
14250950555001809716712898
15917747735007224842888
16333232175002050213812612
1754466611500165536831959
184592603350070654216
192665094599613356583
20208727865007459223607
2125823651500121477201530
2225094237500148905071113
23123645455006473410718
24131126065004293199491
25237387500657122132
269681418488660945343
Time Cost21.5217.40.970.252.342.14

上面是通过一些图片集测的单单是feature detect 的时间，接下来通过一对图片看看feature detect和compute feature descriptor总共花费的时间开销（秒）：

Image pairSIFTSURFORBFAST(SURF)
eiffel-1,13.jpg2.773.220.110.22

可以看到计算descriptor的开销还是很大的，这里仅仅两张图片，所以主要开始是计算描述符，提取是很快的。

下面通过通过两张图片来看这几个算法匹配的效果，1639-1311-697表示图片1，2分别提取了1639，1311个keypoints，其中匹配的有697个。

Image pairSIFTSURFORBFAST(SURF)BRISK
eiffel-1,13.jpg1639/1311/6972802/2606/1243500/500/2511196/1105/586607/491/287

Canny Edge Detection效果：

Find line segments by probabilistic Hough transform：

Harris Corner Detection：

SIFT match效果图：

SURF match效果图：

ORB match效果图：

BRISK match效果图：

代码在这里

参考：
Canny Edge Detector Example
Feature Detection-Canny, HoughLinesP
Harris corner detector Example
BRIEF (Binary Robust Independent Elementary Features)
ORB (Oriented FAST and Rotated BRIEF)

本文标签： SIFT SURF ORB Fast BRISK 特征提取算法比较

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