特征点提取算法列表
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1.提取检测子:在两张待匹配的图像中寻找那些最容易识别的像素点(角点),比如纹理丰富的物体边缘点等。
2.提取描述子:对于检测出的角点,用一些数学上的特征对其进行描述,如梯度直方图,局部随机二值特征等。
检测子和描述子的常用提取方法有:sift, harris, surf, fast, agast, brisk, freak,orb等。
3.匹配:通过各个角点的描述子来判断它们在两张图像中的对应关系。常用方法如 flann
4.去外点:去除错误匹配的外点,保留正确的内点。常用方法有Ransac, GTM。
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).
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