Description: In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance.-In this paper, a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust
Features) is presented. It approximates or even outperforms previously proposed
schemes with respect to repeatability, distinctiveness, and robustness, yet
can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a
distribution-based descriptor) and by simplifying these methods to the
essential. This leads to a combination of novel detection, description, and
matching steps. The paper presents experimental results on a standard
evaluation set, as well as on imagery obtained in the context of a real-life
object recognition application. Both show SURF’s strong performance. Platform: |
Size: 686080 |
Author:yangwei |
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Description: 马氏距离的仿射不变性删除误匹配特征点
对，据此可求取2幅源图像间的仿射变换参数-Mahalanobis distance of the affine invariant features remove the false matching points
Yes, according to the source to obtain two parameter affine transformation between images Platform: |
Size: 1024 |
Author:yimeng |
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Description: 基于局部仿射不变的特征匹配算法，包含全部.m文件，很有价值。-Based on local affine invariant feature matching algorithms, including all. M file of great value. Platform: |
Size: 687104 |
Author:wang |
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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 Platform: |
Size: 2831360 |
Author:李青彦 |
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Description: 这是基于仿射不变特征的图像匹配技术算法，值得参考下载！-This is based on the algorithms of affine invariant features image matching technology, worth considering download! Platform: |
Size: 591872 |
Author:jeffsonfu |
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Description: 仿射图像匹配时候可以用到，需要已知先验知识，然后直接调用就可以了-Affine image matching can be used when the need is known a priori knowledge, then you can directly call the Platform: |
Size: 570368 |
Author:董萍 |
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Description: MSER(删诞mauy stable extremal re舀ons)算法，提出一种对图像的尺度、旋转、仿
射变换更加稳定的区域不变量提取的算法。对于输入图像采用多尺度MSER提取算法，并对
提取的MsERS依据其灰度变换的平稳性对提取区域进行修正。提高了区域提取的可重复性
和匹配概率。-MSER (delete birth mauy stable extremal re scoop ons) algorithm is proposed to image the scale, rotation, affine transformation invariant more stable region extraction algorithm. For input images using multi-scale MSER extraction algorithm, and extracted MsERS according to their gray-scale transformation for the smooth extraction area to be amended. Region extraction improves the reproducibility and matching probability. Platform: |
Size: 673792 |
Author:王明 |
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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 Platform: |
Size: 742400 |
Author:欣欣 |
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Description: 一个演示如何使用“LineMatcher”库的示例,该库用于生成仿射不变匹配直线。详细算法参考Bin Fan, Fuchao Wu and Zhanyi Hu. Line Matching Leveraged by Point Correspondences, In CVPR 2010, pp 390-391. 示例中基于opencv2.2获取匹配点，系统配置为 Win 7 32bits + VS2010 realese
-A demonstration of how to use the example of " LineMatcher" library, the library is used to generate affine invariant straight match. Detailed algorithm reference Bin Fan, Fuchao Wu and Zhanyi Hu. Line Matching Leveraged by Point Correspondences, In CVPR 2010, pp 390-391. Example obtain matching points based opencv2.2, configure the system to " Win 7 32bits+ VS2010" realese Platform: |
Size: 6144 |
Author:Levmay |
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Search - matching affine invariant