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A-local-descriptor-based-on-minutiae-matching-meth
- 一种基于细节点局部描述子的指纹图像匹配方法A local descr iptor based on minutiae matching method for fingerprint image-A local descr iptor based on minutiae matching method for fingerprint image
Gabor-Magnitude-and-Phase-for-Face
- This paper proposes local Gabor XOR patterns (LGXP), which encodes the Gabor phase by using the local XOR pattern (LXP) operator. Then, we in-troduce block-based Fisher’s linear discriminant (BFLD) to reduce the dimensionality of the proposed des
New-folder1
- local descr iptor, normalization of date, precision recall calculation
efficientLBP
- efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found
sift
- 是用于图像处理领域的一种描述子。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。-Is a descr iptor for image processing. This descr iption is scale invariant, can detect the key points in the images, is a local feature descr iptor
surf
- SURF意指 加速的具有鲁棒性的特征，由Bay在2006年首次提出，这项技术可以应用于计算机视觉的物体识别以及3D重构中。SURF算子由SIFT算子改进而来，一般来说，标准的SURF算子比SIFT算子快好几倍，并且在多幅图片下具有更好的鲁棒性。SURF最大的特征在于采用了harr特征以及积分图像integral image的概念，这大大加快了程序的运行时间。-SURF (Speeded Up Robust Feature) is a robust local feature detector,
SIFT
- SIFT是由UBC（university of British Column）的教授David Lowe 于1999年提出， 并在2004年得以完善的一种检测图像关键点（key points , 或者称为图像的interest points(兴趣点) )， 并对关键点提取其局部尺度不变特征的描绘子， 采用这个描绘子进行用于对两幅相关的图像进行匹配（matching）。 目前， SIFT可以说是所有图像局部特征描述特征子 中最火的一个了。-SIFT was developed by David L
SIFT_Matlab_global-context
- SIFT特征检测算法改进，SIFT描述子加入全局向量，对局部信息相似度高的图像检测的误匹配剔除有明显改进-This paper presents a technique for combining global context with local SIFT information to produce a feature descr iptor that is robust to local appearance ambiguity and non-rigid transformation
A novel illumination-robust local descriptor
- Introduce a novel illumination-robust local descr iptor named Sparse Linear Regression Binary (SLRB) descr iptor.