Description: 基于matlab做的三维射线追踪的源代码。供大家交流之用。-based on Matlab do 3D ray tracing the source code. For exchange purposes. Platform: |
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Author:ym |
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Description: The paper describes a new robust real time algorithm for 3D object tracking in a video sequences. Platform: |
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Author:roboter |
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Description: 摘要：在交通场景下进行多目标跟踪时，如何正确检测出车辆间的相互遮挡是影响车辆跟踪结果的关键。针对问题，运用投
影理论分析交通场景的三维几何投影特征．用长方体投影轮廓模型对车辆进行建模，重构其乏维投影轮廓，以进行遮挡的检
测和分离。与以往的方法相比，它在估计出的车辆外形轮廓基础t-进行遮挡检测，不需要匹配操作，计算量较小，并能解决
基于匹配的方法无法对付的初始遮挡问题。用实验验证了该算法的有效性。-In multi—object tracking of traf氍c scene。how to detect the occlusion is a
key problem for vehicle
tracking．A novel vehicle contour based method is prope∞d to deal with this problem．This method firstly extracts the
3D geometry character from the vehicle images according to the projecting theory，establishes the model of the vehicle
with the euboid project contour，and then reconstructs its 3D project contour。detects and segments the occlusion l-e-
sious．Compared with traditional method，it deals with the problem with less computation because it generates the ve·
hide contour firstly and no match calculation is needed．Moreover，this method is able to solve the initial occlusion
problem，which could not be solved with matching based method．The experimental results show that the proposed
method iS more emcient． Platform: |
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Author:christine |
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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. Platform: |
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Author:张博 |
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Description: 学习opencv（中文版）于仕琪、刘瑞祯 清华大学出版社。 opencv是一个开源的计算机视觉库，它为图像处理、模式识别、三维重建、物体跟踪、机械学习和线性代数提供了各种各样的算法-Learning opencv ( Chinese Version) Shi Qi , Rui Zhen , Tsinghua University Press . opencv is an open source computer vision library for image processing , pattern recognition, 3D reconstruction , object tracking , machine learning and linear algebra offers a variety of algorithms
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Author:孟熙 |
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Description: 《学习OpenCV》：计算机视觉是在图像处理的基础上发展起来的新兴学科。OpenCV是一个开源的计算机视觉库，是英特尔公司资助的两大图像处理利器之一。它为图像处理、模式识别、三维重建、物体跟踪、机器学习和线性代数提供了各种各样的算法。
-" Learning the OpenCV" : computer vision is developed on the basis of the image processing emerging disciplines. OpenCV is an open source computer vision library, Intel funded one of the two major image processing tool. For image processing, pattern recognition, 3D reconstruction, object tracking, machine learning provides a wide variety of algorithms and linear algebra. Platform: |
Size: 13513728 |
Author:liujianyu |
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Description: 图像处理 o 梯度 边缘和角点 o 采样 差值和几何变换 o 形态学操作 o 滤波和彩色变换 o 金字塔及其应用 o 连接组件 o 图像和轮廓矩 o 特殊图像变换 o 直方图 o 匹配 结构分析 o 轮廓处理 o 计算几何 o 平面划分 运动分析和对象跟踪 o 背景统计量的累积 o 运动模板 o 对象跟踪 o 光流 o 预估器 模式识别 o 目标检测 照相机定标和三维重建 o 照相机定标 o 姿态估计 o 极线几何 函数列表 参考图像处理注意:本章描述图像处理和分析的一些函数-O gradient edge and corner of the image processing o Sampling difference and geometric transformations o filtering and color transform morphological operations o o pyramid and its application o connected components o image and contour moments o special image transformation o Histogram o matching structure analysis o contour processing o computational geometry o the plane divided motion analysis and object tracking cumulative o Motion templates o o background statistics object tracking o optical flow o predictor pattern recognition o target detection camera calibration and 3D reconstruction o camera calibration o attitude estimates o epipolar geometry function list reference image processing Note: This chapter describes the image processing and analysis functions Platform: |
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Author:diaoguangqiang |
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Description: 在优化粒子滤波跟踪框架下, 设计并实现了一个结合多种图像特征、在多摄像机环境下跟踪人体运动的三
维人体运动跟踪系统1 通过定义三维人体模型、摄像机模型以及观测似然模型, 得到跟踪所需目标函数, 并使用优化
粒子滤波算法进行求解1 实验结果表明, 该系统能够对人体运动进行准确的跟踪和三维重建, 可应用于体育运动分
析和动画制作等领域1-A v ideo-based 3D human body motion t racking system is developed under the optimizat ion part
icle f ilter framework, w hich combines mult iple cues in multiple cameras environment1 By def ining the 3D
body model, camera model and image likelihood model, w e can define the tracking object funct ion and resolve
it using opt imized part icle f ilter1 T he experiment s show that t racking and reconstructing result s made
by our system are accurate and effect ive1 This system can be applied to several areas such as sport ing mot ion
analysis and human animat ion1 Platform: |
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Author:cp |
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Description: 《学习OpenCV》：计算机视觉是在图像处理的基础上发展起来的新兴学科。OpenCV是一个开源的计算机视觉库，是英特尔公司资助的两大图像处理利器之一。它为图像处理、模式识别、三维重建、物体跟踪、机器学习和线性代数提供了各种各样的算法。
本书由OpenCV发起人所写，站在一线开发人员的角度用通俗易懂的语言解释了OpenCV的缘起和计算机视觉基础结构，演示了如何用OpenCV和现有的自由代码为各种各样的机器进行编程，这些都有助于读者迅速入门并渐入佳境，兴趣盎然地深入探索计算机视觉领域。-" Learning OpenCV" : computer vision is developed on the basis of image processing on the emerging discipline. OpenCV is an open source computer vision library, is one of two image processing tool funded by Intel Corporation. It is an image processing, pattern recognition, 3D reconstruction, object tracking, machine learning and offer a variety of linear algebra algorithms. Promoters book written by OpenCV, from the perspective of front-line developers in plain language to explain the origin and infrastructure OpenCV computer vision, and demonstrates how to use OpenCV and existing free code for a variety of machine programming, which will help readers get started quickly and getting better, with interest-depth exploration of the field of computer vision. Platform: |
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Author:joyce tse |
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Description: 使用特征点来代表图像的内容，运动目标跟踪，物体识别，图像配准，全景图像拼接，三维重建-The use of feature points to represent the content of the image, moving object tracking, object recognition, image registration, image mosaics, 3D reconstruction
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Author:孙龙 |
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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. Platform: |
Size: 90112 |
Author:dhivya |
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Description: SIFT匹配（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，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。-Matching SIFT (Scale invariant feature transform, Scale invariant feature transform) is a computer vision algorithm is used to detect and describe the local characteristics of image, it seek extreme value point in the space Scale, and extract its location, Scale and rotation invariant, its application areas include perception and object recognition, robot map navigation, image stitching, 3 d modeling, gesture recognition, image tracking and action.
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Author:陈天华 |
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Description: 擴增實境技術是在真實視訊影像中加入虛擬物件，並透過追蹤與定位技術，可以與人們產生良好之互動效果。在視覺追蹤應用領域裡，可分為標記與無標記兩類應用。標記識別技術較為成熟，目前擴增實境開發平台以採用標記識別為主；至於無標記則侷限在特定方法之識別追蹤應用領域，例如樂高玩具利用包裝盒上之印刷圖片當作辨識物件。面對無標記擴增實境之應用日趨重要，且必須因應不同物件採用不同特徵之識別追蹤方法來達成無標記擴增實境之應用。而目前擴增實境平台並不提供模組化方式來替換識別追蹤方法，因此本文提出無標記擴增實境實驗平台，以現有擴增實境套件ARtoolKit為基礎，整合OpenCV與OpenGL函式庫，並採用模組化方式來設計視覺追蹤方法，做為驗證無標記擴增實境識別追蹤方法之平台，且透過視窗操作選擇不同視覺追蹤模組來呈現各式追蹤方法，以利分析驗證追蹤效能。-The technique of augmented reality (AR) is to augment 3D virtual objects into real images. Individual can interact with 3D virtual objects using tracking and registration methods. Visual tracking is the most popular tracking approach used in AR system, and markers are simply and generally used for identification and tracking. Moreover, natural feature or marker-less identification and tracking is getting more and more important and can be widely used in numerous applications. Therefore, many natural feature extraction and object tracking schemes are developed to efficiently identify and track natural objects. However, few of platforms are designed to verify different tracking algorithms for AR system. In this thesis, a novel tracking verification platform for AR environment, ARStudio, is proposed. ARStudio is on the basis of ARToolKit, and integrates the library of OpenCV and OpenGL. Furthermore, we modularize each component such as image capture, image transform, visual tracking, imag Platform: |
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Author:鍾德煥 |
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Description: 目前擴增實境技術相關應用大部分以使用標記為主，但各式應用需求與日俱增，無標記(markerless)擴增實境技術使用上更具彈性，不必受限於標記的使用，因此應用層面更廣。視覺追蹤技術是擴增實境系統重要底層核心技術之一，但使用視覺追蹤技術在實際應用上易受到追蹤物件本身及外觀變化之影響，因此本文提出適用於無標記擴增實境應用之物件追蹤方法，能有效追蹤各式真實物件。首先框選設定追蹤物件；接著擷取物件特徵值，藉由特徵值比對以持續追蹤物件，並利用金字塔L-K光流法以縮短比對運算時間；最後經由2D-3D座標轉換，將3D虛擬物件疊加至真實場景。經由實驗證明本文所提之方法能有效追蹤各式真實物件，並能適用於無標記擴增實境相關應用。-Augmented Reality technology currently most relevant applications to use mark-based, but the increasing demand for all kinds of applications, without marking (markerless) amplification technology more flexible use of reality, not necessarily limited to the use of tags, therefore the application level broader. Visual tracking technology is one of the important augmented reality system underlying core technology, but the use of visual tracking technology in practical applications susceptible to track changes and appearance of the object itself, and therefore in this paper applies to Amplification unmarked objects of reality application tracing method can effectively track all kinds of real objects. First, set the track object marquee then retrieve objects eigenvalues by eigenvalue than to keep track of objects, and uses of the pyramid LK optical flow method to shorten the computation time than on and finally converted via 2D-3D coordinates of the 3D virtual object superimposed to the r Platform: |
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Author:鍾德煥 |
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Description: Many applications require tracking of complex 3D objects. These
include visual servoing of robotic arms on specific target objects, Augmented
Reality systems that require real-time registration of the object
to be augmented, and head tracking systems that sophisticated interfaces
can use. Computer Vision offers solutions that are cheap, practical
and non-invasive. Platform: |
Size: 2057216 |
Author:kblykkk
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Description: 本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过
大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、
立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on the Python language.A large number of examples carefully analyze object recognition, content based image search, optical character recognition, optical flow, tracking, 3D reconstruction,Stereoscopic imaging, augmented reality, attitude estimation, panoramic creation, image segmentation, noise reduction, image grouping and other techniques.) Platform: |
Size: 17972224 |
Author:sycai
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Description: 《Python计算机视觉编程》是计算机视觉编程的实践指南，依赖Python语言讲解了基础理论与算法，并通过大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。另外，书中附带的练习还能让读者巩固并学会应用编程知识。("Python Computer Vision Programming" is a practical guide to computer vision programming. It relies on the Python language to explain basic theories and algorithms, and analyzes object recognition, content-based image search, optical character recognition, optical flow method, tracking, and detailed analysis through a large number of examples. 3D reconstruction, stereo imaging, augmented reality, pose estimation, panorama creation, image segmentation, noise reduction, image grouping and other technologies. In addition, the exercises included in the book also allow the reader to consolidate and learn the knowledge of application programming.) Platform: |
Size: 17479680 |
Author:稳字经 |
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Search - 3D object tracking