Description: 计算机视觉是在图像处理的基础上发展起来的新兴学科。计算机视觉从信息处理的层次研究视觉信息的认知过程，研究视觉信息处理的计算理论、表达与计算方法。本书系统地介绍了计算机视觉的重要理论与算法，包括图像特征提取，摄像机定标，立体视觉，运动视觉(或称序列图像分析)，由图像灰度恢复三维物体形状的方法，物体建模与识别方法以及距离图像分析方法等。-in computer vision image processing on the basis of the development of a new subject. Computer processing of visual information from the study on the levels of visual information cognitive process, the study of visual information processing theory. Expression and calculation methods. The book systematically introduces computer vision of the important theory and algorithms, including image feature extraction, camera calibration, stereo vision, sports vision (or image sequence analysis), gray-scale images from 3D object shape, Object Modeling and Recognition and the distance between image analysis methods. Platform: |
Size: 13978253 |
Author:谢志东 |
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Description: 计算机视觉是在图像处理的基础上发展起来的新兴学科。计算机视觉从信息处理的层次研究视觉信息的认知过程，研究视觉信息处理的计算理论、表达与计算方法。本书系统地介绍了计算机视觉的重要理论与算法，包括图像特征提取，摄像机定标，立体视觉，运动视觉(或称序列图像分析)，由图像灰度恢复三维物体形状的方法，物体建模与识别方法以及距离图像分析方法等。-in computer vision image processing on the basis of the development of a new subject. Computer processing of visual information from the study on the levels of visual information cognitive process, the study of visual information processing theory. Expression and calculation methods. The book systematically introduces computer vision of the important theory and algorithms, including image feature extraction, camera calibration, stereo vision, sports vision (or image sequence analysis), gray-scale images from 3D object shape, Object Modeling and Recognition and the distance between image analysis methods. Platform: |
Size: 13977600 |
Author:谢志东 |
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Description: 3d图像识别的英文文章，推荐图像识别的朋友参考下-3d image recognition of the English article, a friend recommended to identify a reference image under the Platform: |
Size: 239616 |
Author:wenran |
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Description: Patent:SYSTEM AND METHOD FOR 3D OBJECT RECOGNITION. The present invention provides a system and method for recognizing a 3D object in a single camera image and for determining the 3D pose of the object with respect to the camera coordinate system. In one typical application, the 3D pose is used to make a robot pick up the object. A view-based approach is presented that does not show the drawbacks of previous methods because it is robust to image noise, object occlusions, clutter, and contrast changes. Furthermore, the 3D pose is determined with a high accuracy. Finally, the presented method allows the recognition of the 3D object as well as the determination of its 3D pose in a very short computation time, making it also suitable for real-time applications. These improvements are achieved by the methods disclosed herein. Platform: |
Size: 591872 |
Author:yangwei |
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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: |
Size: 8782848 |
Author:张博 |
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Description: SURF_DETECTOR
SURF (Speeded Up Robust Feature) is a robust image detector and descriptor, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. Platform: |
Size: 1024 |
Author:Kim |
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Description: 基于模型的三维物体识别，实现了完整的基于特征面对三维物体匹配与识别系统-Model-based three-dimensional object recognition, to achieve a complete three-dimensional object matching and recognition system based on the characteristics of the face Platform: |
Size: 142336 |
Author:许俊峰 |
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Description: 基于PCL识别模型的3D物体识别源码。-This tutorial aims at explaining how to perform 3D Object Recognition based on the pcl_recognition module. Platform: |
Size: 4096 |
Author:周振 |
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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: 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
Platform: |
Size: 3072 |
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.
Platform: |
Size: 10620928 |
Author:陈天华 |
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Description: SURF意指 加速的具有鲁棒性的特征，由Bay在2006年首次提出，这项技术可以应用于计算机视觉的物体识别以及3D重构中。SURF算子由SIFT算子改进而来，一般来说，标准的SURF算子比SIFT算子快好几倍，并且在多幅图片下具有更好的鲁棒性。SURF最大的特征在于采用了harr特征以及积分图像integral image的概念，这大大加快了程序的运行时间。-SURF (Speeded Up Robust Feature) is a robust local feature detector, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images.
Platform: |
Size: 2418688 |
Author:草 |
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Description: 该算法可以应用于计算机视觉的物体识别以及3D重构中。-The algorithm can be applied to object recognition in computer vision and 3D Reconstruction. Platform: |
Size: 136192 |
Author:zhaoxiaoming |
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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: 本书介绍Python的计算机视觉编程，用清晰的Python示例，细致讲解对象识别、3D重建、立体图像、增强现实及其他计算机视觉应用技巧，给出了分析图像的工具与方法。内容：机器人导航、医学图像分析；图像映射与变换；多图像的3D重建；用聚类方法基于相似性和内容组织图像；基于视觉内容的图像检索技巧；实现图像内容分类和对象识别的算法；通过Python接口访问常用的OpenCV库。(This book introduces Python's computer vision programming. With clear Python examples, it elaborates on object recognition, 3D reconstruction, stereo image, augmented reality and other computer vision application techniques, and gives tools and methods for image analysis. Contents: Robot navigation, medical image analysis; image mapping and transformation; multi-image 3D reconstruction; image organization based on similarity and content using clustering method; image retrieval techniques based on visual content; algorithm for image content classification and object recognition; access to common OpenCV library through Python interface.) Platform: |
Size: 5047296 |
Author:qhxinshou |
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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 recognition