Description: asift,全名是 affine scale-invariant feature transform ，即 仿射性尺度不变特征变换，是用来提取较大仿射扭曲不变的特征区域的一种方法，相信对大家有用-asift, full name is the affine scale-invariant feature transform, that is, of scale-invariant features of affine transform, is used to extract more of the characteristics of affine distortion of the same region, a method useful for all of us believe that ~ ~ ~ Platform: |
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Author:txy |
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Description: 运用多尺度自卷积计算图像的仿射不变矩的方法，该方法新颖，具有一定的创新性-Using multi-scale image from the convolution calculation of affine invariant moment method, which is novel, has some innovative Platform: |
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Author:夏雨 |
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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: |
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Author:yangwei |
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Description: 一种比sift效果更好的算法，可实现图像在几乎任意仿射变化下的特征提取，而且特征点比SIFT多，是09年最新的算法-asift, full name is the affine scale-invariant feature transform, that is, of scale-invariant features of affine transform, is used to extract more of the characteristics of affine distortion of the same region, a method useful for all of us believe that ~ ~ ~ Platform: |
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Author:yimeng |
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Description: 一种基于最大熵和部件的物体检测方法。在人脸检测上的应用取得了很大的准确率提升。-This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance
of scale- or affine-invariant keypoints. Object classes are represented using a dictionary of composite semi-local parts, or groups of neighboring keypoints with stable and
distinctive appearance and geometric layout. A discriminative
maximum entropy framework is used to learn the posterior
distribution of the class label given the occurrences
of parts from the dictionary in the training set. Experiments
on two texture and two object databases demonstrate the
effectiveness of this framework for visual classification. Platform: |
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Author:刘馨惠 |
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Description: 仿射不变特征提取 多尺度自卷积方法-The Multi-Scale Autoconvolution (MSA) method offers a novel way of approaching this problem. The method provides affine invariant features with only moderate computational complexity and does not require any other segmentation steps than background elimination. Platform: |
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Author:chenfeng |
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Description: 整合了模式识别领域中几种经典的关于区域识别的文章，对于研究模式识别的同学，会有很大帮助-Integrated field of pattern recognition several classic articles on regional recognition, pattern recognition for research students, there will be a great help Platform: |
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Author:房英明 |
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Description: 1999年British Columbia大学大卫.劳伊（David G.Lowe）教授总结了现有的基于不变量技术的特征检测方法，并正式提出了一种基于尺度空间的、对图像缩放、旋转甚至仿射变换保持不变性的图像局部特征描述算子－SIFT（尺度不变特征变换），这种算法在2004年被加以完善。
-University of British Columbia 1999, David Rowe (David G. Lowe) summed up the professor is not variable based on existing technology, feature detection methods, and formally proposed based on scale space for image scaling, rotation or even affine transform the image to maintain local features invariant description operator-SIFT (Scale Invariant Feature Transform), this algorithm is to be improved in 2004. Platform: |
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Author:chenping |
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Description: 基于内容的图像检索技术的关键在于特征提取，是利用图像的颜色、形状、纹理、轮廓、对象的空间关系等客观独立的存在于图像中的基本视觉特征作为图像的索引，计算查询图像和目标图像的相似距离，按相似度匹配进行检索。综合国内外研究现状，可将基于内容的图像检索技术分为如下几种类型:基于颜色特征的检索、基于纹理特征的检索、基于形状及区域的检索、基于空间约束关系的检索。-Based on comparing various affine invariant regional basis, selection of image stabilization exremum area biggest content segmentation and extraction. It has the affine invariants, the neighboring territory, stability and multi-scale characteristics, but also because of regional only by grey value of decision, so the size relations is not sensitive illumination change. In images all the pixel, then through sorting for barrel separated binary tree forest-- set the extreme area all obtained images and construct component tree, finally through the biggest stable delay-independent conditions MSER area is MSER, obtain the area without rules boundary shape of its, in order to facilitate to quantification description, using covariance matrix region neat optimization, made the final output of extreme value for the oval areas regional. Platform: |
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Author:陈利华 |
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Description: Sift算子最早是由D.G.Lowe于1999年提出的，当时主要用于对象识别。
2004年D.G.Lowe对该算子做了全面的总结，并正式提出了一种基于尺度空间的、对图像缩放、旋转甚至仿射变换保持不变性的图像局部特征描述算子sift（Scale Invariant Feature Transform)算子，即尺度不变特征变换。-Sift child the first count by DGLowe in 1999, was mainly used for object recognition.
Years DGLowe of the operator to do a comprehensive summary of, and formally proposed a scale space-based, image scaling, rotation, or even maintain the invariance of the affine transformation image characterization operator sift (the Scale Invariant Feature the Transform ) operator, ie, scale invariant feature transform Platform: |
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Author:江涛 |
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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: |
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Author:李青彦 |
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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: |
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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: |
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Author:欣欣 |
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Description: 仿射不变Harris, Laplacian, det(Hessian) and Ridge 特征点检测
参考文献：An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.-Matlab code for detecting Affine spatial interest points. Includes Harris, Laplacian, det(Hessian) and Ridge interest point operators in combination with spatial scale selection based on the extrema of scale-normalized Laplacian and affine adaptation basen on second-moment matrix. Scale and shape adaptation are optional and disjoint.
Zip archive: affintpoints.zip
Ref: An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.
What is in the package:
1) ineterst point detection of different kinds (Harris, Laplace, det(H), Ridge)
2) scale, shape and position adaptation procedure
3) demo examples and a script for batch-mode computation and saving of the results
what is not in the package:
- no rotation estimation
- no region descriptor computation Platform: |
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Author:灵台斜月 |
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Description: 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、也保持一定程度的稳定性.本程序采用MATLAB和c语言联合编程。(SIFT feature is a local feature of the image. It keeps invariant to rotation, scale scaling and luminance change, and also keeps a certain degree of stability to the change of view angle, affine transformation. This program is programmed by MATLAB and C language.) Platform: |
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Author:杨秀洪 |
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Search - Scale Affine Invariant