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Description: SURF spped up robust features openCV 实现
为源代码 Fast SIFT
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Author: hu |
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Description: sift算法的代码，可以进行特征点的提取和匹配，速度不如GPU的快-sift algorithm code, can feature points extraction and matching, slower than GPU fast
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Size: 3783680 |
Author: alenw |
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Description: 边界重叠图像的一种快速拼接算法,计算机实现边界重叠图像的自动配准.-Border overlapping images of a fast stitching algorithm, the computer realization of the boundary overlap automatic image registration.
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Size: 212992 |
Author: 依静 |
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Description: SURF spped up robust features openCV 实现
为源代码 Fast SIFT-SURF spped up robust features openCV the realization of the source code for the Fast SIFT
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Size: 683008 |
Author: hu |
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Description: 采用简化SIFT算法实现快速图像匹配，相当实用的文章-Simplified fast SIFT image matching algorithm
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Author: zy |
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Description: A fast matlab code for image feature matching
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Size: 52224 |
Author: sanjeev |
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Description: The code efficiently computes the Earth Mover s Distance (EMD) between two histograms or sparse histograms (signatures). The EMD is also known as Mallows, 1st Wasserstein, Monge-Kantorovich, Match and Transporatation distances. The approach was described in the paper:
"Fast and Robust Earth Mover s Distance" [, ].
EMD-HAT (a better definition of EMD for non-normalized histograms) was presented in the paper:
"A Linear Time Histogram Metric for Improved SIFT Matching" [, ]. -The code efficiently computes the Earth Mover s Distance (EMD) between two histograms or sparse histograms (signatures). The EMD is also known as Mallows, 1st Wasserstein, Monge-Kantorovich, Match and Transporatation distances. The approach was described in the paper:
"Fast and Robust Earth Mover s Distance" [, ].
EMD-HAT (a better definition of EMD for non-normalized histograms) was presented in the paper:
"A Linear Time Histogram Metric for Improved SIFT Matching" [, ].
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Size: 201728 |
Author: ning |
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Description: fast SIFT code using GPU
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Size: 5306368 |
Author: Yoo,seok bong |
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Description: 基于SIFT快速匹配算法，对障碍物进行检测，提取障碍物，双目测距等。能指出指定范围内的障碍物。-SIFT-based fast matching algorithm, obstacle detection, extraction of obstacles, such as binocular distance. Can point to obstacles within the specified range.
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Size: 27874304 |
Author: 罗铃 |
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Description: 一种改进的快速sift特征匹配算法，对学习sift算法很有帮助-An improved fast feature matching algorithm sift
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Size: 149504 |
Author: feng hw |
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Description: 快速SIFT算法，处理迅速。使用OPENCV-Fast SIFT algorithm to deal with quickly. Use OPENCV
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Size: 4008960 |
Author: 罗铃 |
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Description: 用于提取图像的sift特征，计算速度十分快速，有效-Sift for extracting image features, the calculation speed is very fast and effective
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Size: 6084608 |
Author: 石磊 |
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Description: Mosiacking images fast using source code that doesn t use RANSAC or SIFT
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Size: 905216 |
Author: loke |
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Description: 实现SIFT算子，效率高，但还是有改进的空间。-realize SIFT algorithm，fast and efficient，but it also can be developed
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Size: 440320 |
Author: 何德龙 |
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Description: 摘要：针对SIFT算法在圆心检测应用中的特点，分析了SIFT算法中存在的冗余步骤，提出对其进行简化改进：
估计圆心所在的金字塔层，删减金字塔中多余的层数建立，并且通过提高滤除低对比度特征点阈值的办法减少
背景特征点的生成，去掉了特征点辅方向分配和建立辅方向特征点这一步骤。分别对计算机模拟生成的标准
圆和实际的PCB定位标志图像做了圆心检测实验，实验表明，SIFT算法改进后在没有降低其定位精度及对图
像的模糊、光照、仿射等变换适应性的情况下，大大缩短了检测时间，提高了检测效率。
关键词：圆心定位；尺度不变特征变换(sI )算法；特征匹配；冗余-Abstract：In allusion to the characteristics of the SIFT algorithm in the application of circle center 1ocation，redundant proce—
dure is analyzed and a novel predigested and improved method is proposed．By estimating which pyramid octave the circle
center is in，eliminating the redundancy pyramid octaves，and reducing the keypoints generated from the background by in
creasing the threshold for filtering out the keypoints with low contrast，the steps of assigning assistant orientations of the
keypoints and generating keypoints with assistant orientation has been leaved out．Circle center detection experiments have
been carried out for both standard drcles generated by the computer and the real PCB location mark images．The experiment
results show that，wi thout any penalty of the location accuracy，or robustness to blur，illumination，affine transformation，the
detection efficiency has been improved dramatically with the proposed improved method ．
Key words：circle center location；scale inva
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Size: 293888 |
Author: 李齐贤 |
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Description: 1 SIFT 发展历程
SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。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。
2 SIFT 主要思想
SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。
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
本包内容为sift算法matlab源码-1 SIFT course of development
SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement.
2 the SIFT main idea
The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant.
3 the main features of the SIFT algorithm:
a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability.
b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23].
c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors.
d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements.
e) The scalability can be very convenient fe
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Size: 2831360 |
Author: 李青彦 |
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Description: 图像匹配与识别 快速 匹配率高 需要用matlab 运行-Image matching and recognition fast matching rate need to run using matlab
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Size: 11563008 |
Author: 李猛 |
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Description: SIFT(Scale Invariant Feature Transform)即尺度不变特征变换，是 D. G.Lowe 在 1999 年提出的一种基于图像局部特征的描述算子，并于 2004年做了完善。SIFT算法是一种基于线性尺度空间，对图像缩放、旋转甚至仿射变换保持不变的局部特征描述算子，因此被广泛地应用于机器人定位、导航和地图生成中。-This paper presents a method for extracting distinctive invariant features from images that can be used
to perform reliable matching between different views of an object or scene. The features are invariant to image scale
and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in
3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm,followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to r
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Size: 742400 |
Author: 欣欣 |
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Description: 用FAST算法检测角点，用SIFT特征描述符描述角点(FAST algorithm is used to detect corner points and SIFT feature descriptors are used to describe corner points)
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Size: 41984 |
Author: erinliu
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Description: a fast sift algorithme
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Size: 575488 |
Author: ssssffff
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