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Description: IEEE 802.11a-1999 (8802-11:1999/Amd 1:2000(E)), IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 1: High-speed Physical Layer in the 5 GHz band
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Size: 970641 |
Author: 周金喜 |
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Description: 802.11b-1999/Cor1-2001, IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 2: Higher-speed Physical Layer (PHY) extension in the 2.4 GHz band—Corrigendum1
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Size: 413053 |
Author: 周金喜 |
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Description: IEEE 802.11d-2001, Amendment to IEEE 802.11-1999, (ISO/IEC 8802-11) Information technology--Telecommunications and information exchange between systems--Local and metropolitan area networks--Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Specification for Operation in Additional Regulartory Domains
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Size: 474943 |
Author: 周金喜 |
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Description: IEEE 802.11i-2004 Amendment to IEEE Std 802.11, 1999 Edition (Reaff 2003). IEEE Standard for Information technology--Telecommunications and information exchange between system--Local and metropolitan area networks?Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications--Amendment 6: Medium Access Control (MAC) Security Enhancements
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Size: 1652121 |
Author: 周金喜 |
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Description: In the last twenty years, the design of efficient function optimizers has been a crucial topic of research work. Many theoretical and practical research problems involve combinatorial optimization, which is obtaining the optimal solution among a finite set of alternatives. Such optimization problems are notoriously difficult to solve, one of the primary reasons being that in most applications the number of alternatives is extremely large and only a fraction of them can be considered within a reasonable amount of time (Shi et al, 1999). Indeed, such problems are often non-differentiable or multimodal, causing difficulties for classical gradient and random search methods, which are limited to simple unimodal functions and which are thus inappropriate to apply to such difficult problems. The seminal methods to deal with this problem are called Deterministic Optimization Methods (DOMs) (Fletcher, 1980). There are two major drawbacks when using DOMs: their high computational complexity and the possibility of becoming trapped in a local optimum. Moreover, DOMs are difficult to program, as they require a deep mathematical knowledge.
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Size: 12082 |
Author: dwenhcil@gmail.com |
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Description: ulm大学1996-1999年的竞赛题和解题报告，测试数据-1996-1999 University of Ulm race title and problem-solving reports, test data
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Size: 906240 |
Author: 杨令云 |
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Description: IEEE 802.11a-1999 (8802-11:1999/Amd 1:2000(E)), IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 1: High-speed Physical Layer in the 5 GHz band -IEEE 802.11a-1999 (8802-11:1999/Amd 1:2000 (E)), IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements-Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications-Amendment 1: High-speed Physical Layer in the 5 GHz band
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Size: 970752 |
Author: 周金喜 |
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Description: 802.11b-1999/Cor1-2001, IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 2: Higher-speed Physical Layer (PHY) extension in the 2.4 GHz band—Corrigendum1-802.11b-1999/Cor1-2001, IEEE Standard for Information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements-Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications- Amendment 2: Higher-speed Physical Layer (PHY) extension in the 2.4 GHz band-Corrigendum1
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Size: 412672 |
Author: 周金喜 |
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Description: IEEE 802.11d-2001, Amendment to IEEE 802.11-1999, (ISO/IEC 8802-11) Information technology--Telecommunications and information exchange between systems--Local and metropolitan area networks--Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Specification for Operation in Additional Regulartory Domains
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Size: 475136 |
Author: 周金喜 |
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Description: IEEE 802.11i-2004 Amendment to IEEE Std 802.11, 1999 Edition (Reaff 2003). IEEE Standard for Information technology--Telecommunications and information exchange between system--Local and metropolitan area networks?Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications--Amendment 6: Medium Access Control (MAC) Security Enhancements
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Size: 1651712 |
Author: 周金喜 |
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Description: 802.1Q(Virtual Bridged Local Area Networks)协议，供参考-802.1Q(Virtual Bridged Local Area Networks)
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Size: 1080320 |
Author: weixianke |
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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.
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Author: chenping |
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Description: SIFT特征（Scale-invariant feature transform，尺度不变特征转换)是一种电脑视觉的算法用来侦测与描述影像中的局部性特征，它在空间尺度中寻找极值点，并提取出其位置、尺度、旋转不变量，此算法由 David Lowe 在1999年所发表，2004年完善总结。其应用范围包含物体辨识、机器人地图感知与导航、影像缝合、3D模型建立、手势辨识、影像追踪和动作比对。-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving.
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Size: 8782848 |
Author: 张博 |
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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: 一种常用于图像特征描述的一种描述子，全称尺度不变特征变换-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
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Size: 4096 |
Author: 程小青 |
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Description: IEEE 802.11a-1999 or 802.11a was an amendment to the IEEE 802.11 wireless local network specifications that defined requirements for an orthogonal frequency division multiplexing (OFDM) communication system. It was originally designed to support wireless communication in the unlicensed national information infrastructure (U-NII) bands (in the 5–6 GHz frequency range) as regulated in the United States by the Code of Federal Regulations, Title 47, Section 15.407.
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Size: 64512 |
Author: Anisur Rahman |
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Description: 取得互联网时间，设置本地电脑时间,我自己开发的工具，主要是因为电脑开机时间总是1999年，让软件自动重置。-Get Internet time, set the local computer time
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Size: 24576 |
Author: 大海 |
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Description: Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia.-Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1]
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
The algorithm is patented in the US the owner is the University of British Columbia.
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Size: 90112 |
Author: dhivya |
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Description: Object matching demo based on Lowe, D.G. 1999. Object recognition local scale-invariant features.
In International Conference on Computer Vision, Corfu, Greece, pp. 1150?157.
uses SURF instead of SIFT -Object matching demo based on " Lowe, DG 1999. Object recognition local scale-invariant features. In International Conference on Computer Vision, Corfu, Greece, pp. 1150? 157. Uses SURF instead of SIFTObject matching demo based on Lowe, D.G. 1999. Object recognition local scale-invariant features.
In International Conference on Computer Vision, Corfu, Greece, pp. 1150?157.
uses SURF instead of SIFT
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Size: 3018752 |
Author: knwi7851 |
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Description: SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。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。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。-SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.
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Author: terigen |
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