Description: 深入浅出介绍计算机视觉的最新动态。内容包括：
* Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibration
* Extracting camera motion and scene structure from image sequences
* Robust regression for model fitting using M-estimators, RANSAC, and Hough transforms
* Image-based lighting for illuminating scenes and objects with real-world light images
* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more
* Face detection, alignment, and recognition--with new solutions for key challenges
* Perceptual interfaces for integrating vision, speech, and haptic modalities
* Development with the Open Source Computer Vision Library (OpenCV)
* The new SAI framework and patterns for architecting computer vision applications Platform: |
Size: 12192309 |
Author:kankan |
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Description: 深入浅出介绍计算机视觉的最新动态。内容包括：
* Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibration
* Extracting camera motion and scene structure from image sequences
* Robust regression for model fitting using M-estimators, RANSAC, and Hough transforms
* Image-based lighting for illuminating scenes and objects with real-world light images
* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more
* Face detection, alignment, and recognition--with new solutions for key challenges
* Perceptual interfaces for integrating vision, speech, and haptic modalities
* Development with the Open Source Computer Vision Library (OpenCV)
* The new SAI framework and patterns for architecting computer vision applications-Easy to introduce the latest developments in computer vision. Include:* Camera calibration using 3D objects, 2D planes, 1D lines, and self-calibration* Extracting camera motion and scene structure from image sequences* Robust regression for model fitting using M-estimators, RANSAC, and Hough transforms* Image-based lighting for illuminating scenes and objects with real-world light images* Content-based image retrieval, covering queries, representation, indexing, search, learning, and more* Face detection, alignment, and recognition- with new solutions for key challenges* Perceptual interfaces for integrating vision, speech, and haptic modalities* Development with the Open Source Computer Vision Library (OpenCV)* The new SAI framework and patterns for architecting computer vision applications Platform: |
Size: 12191744 |
Author:kankan |
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Description: Lire 0.7 is a major release fixing a lot of bugs and introducing several new features including new descriptor, a simplified way to use descriptors by introducing new generic searchers and indexers as well as an generalized interface for image descriptors. There are also several improvements in indexing and search speed (especially in autocolorcorrelogram). Furthermore retrieval performance was optimized based on the Wang 1000 data set. If you use Lire 0.7 to update an existing version, please make sure that your indices are created newly from scratch. Platform: |
Size: 155648 |
Author:chenglong |
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Description: 随着多媒体、网络技术的迅速发展，图像信息的应用日益广泛，对规模越来越大的图像数据库、可视信息进行有效的管理成为迫切需要解决的问题，灵活、高效、准确的图像检索策略是解决这一问题的关键技术之一。因此，基于内容的图像检索已成为国内外学者研究的主要热点问题，并取得了不少的成果。
本文主要对当今热门的基于内容的图像检索技术进行了研究，重点对它的算法进行研究。在半年的时间里，通过查阅很多相关的资料，并认真学习了基于内容的图像检索的基本理论，特别是深入研究了颜色直方图理论和累加直方图算法，最后在MATLAB平台下编程实现此系统，该系统可以实现基本图像检索的功能，根据用户输入的样本图像来与图像库中的图像进行特征匹配，然后找出与样本图像距离比较小的若干幅图像，并按照图像之间的距离由小到大的顺序显示给用户。
经过对该系统进行反复的调试运行后，该系统所实现的功能基本达到了设计目标，并且运行良好。当用户提供出所要查询的关键图后，系统就可以从用户提供的图像库中检索到与关键图相似的图片并排序返回给用户，达到了预期效果。
-With the rapid development of the multimedia and the network technology, the image information becomes more widely available, increasing the size of the image database, visual information for effective management of an urgent need to address the problem, flexible, efficient and accurate image retrieval strategy solve this problem one of the key technologies. The researchers are so keen on Content-Based Image Retrieval that they have made much progress.
In this paper, today s popular content-based image retrieval technology is analyzed. And it mainly focuses on the research of its algorithm. In a period of half a year, Through access to relevant information and to seriously study the content-based image retrieval of the basic theory, in particular, in-depth study of the color histogram theory and cumulative histogram algorithm. Finally, this system should be implemented under the platform of the MATLAB by programming. In this system, the basic image retrieval functions can be achieved. Platform: |
Size: 380928 |
Author:qichao |
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Description: 提出了一种有效用于抽取特征、索引和检索彩色图像的技术途径. 通过提取图像的颜色不变量, 建立相应
的色度直方图(hue h istogram) 来表示图像的颜色分布特征. 为了描述图像中对象的位置及方向特征, 首先计算图像的色度轮廓并对其进行Radon 变换, 然后计算相应的“空间直方图”.-Proposes an effective feature for the extraction, indexing and retrieval of color images of the technical ways. By extracting the image color is not a variable, set up a corresponding color histogram (hue h istogram) to represent the color distribution of the image. In order to describe the image the location and orientation of objects characteristic of the first calculation of the image color profile and gain Radon transform, and then calculates the corresponding " space histogram." Platform: |
Size: 621568 |
Author:史习云 |
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Description: 1．分析研究了基于内容的图像检索系统的工作原理，关键技术如：纹
理、形状等图像底层特征的描述方法， 图像间的相似性度量方法，
图像库索引机制等。
2．研究了基于纹理特征的图像检索方法，并提出了一种基于NSCT 变
换的纹理特征提取方法。通过对SAR 图像及相关图像进行NSCT
分解，计算不同尺度不同方向上的系数幅度序列的均值，标准方差
和三阶中心矩，以此构成特征向量来描述图像的纹理。实验证明本
文提出的采用NSCT 算法有较好的特征提取效果，引入三阶中心矩
作为特征向量优于只使用均值和方差的组合特征，提高了图像检索
的查准率。
3．研究了基于形状特征的图像检索方法，并提出一种基于NSCT变换
的形状特征提取方法。把改进型Canny算子和NSCT变换相结合，先
对SAR图像及相关图像运用改进型Canny算子提取边缘，在此基础
上再进行NSCT变换，把图像的形状信息分解到不同尺度不同方向
上，从而保留各个频率分量，减少了图像形状信息的丢失。-1. First we analyze and study the principle of image retrieval system and
key techniques and algorithms of CBIR, such as the low-level feature
descriptions including texture, shape, the similarity measure between
images, the indexing methods and so on
2. Researching on the texture-based image retrieval algorithm, we
propose an algorithm of texture feature extraction based on the
Nonsubsampled Contourlet transform in this thesis. The image is
decomposed by the Nonsubsampled Contourlet transform. The mean,
standard deviation and third central moment of the magnitude of the
Nonsubsampled Contourlet coefficients at different scales and directions
are computed to extract the texture feature vector.Experiment proves the
third central moment added in NSCT arithmetic is overperformded than
only use the mean and standard deviation, and precision ratio has
improved.
3. Researching on the shape-based image retrieval algorithm, we propose
an algorithm of shape feature extraction bas Platform: |
Size: 401408 |
Author:周二牛 |
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Description: In many areas of commerce, government, academia, and hospitals, large collections of digital images are
being created. Many of these collections are the product of digitizing existing collections of analogue photographs,
diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections
was by keyword indexing, or simply by browsing. Digital images databases however, open the way to
content-based searching. In this paper we survey some technical aspects of current content-based image
retrieval systems. Platform: |
Size: 2122752 |
Author:严 |
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Description: 图像拼接技术可广泛应用于诸多领域，在宇宙空间探测、海底勘测、医学、气象、地质勘测、军事、视频压缩和传输、档案的数字化保存、视频的索引和检索、物体的三维重建、数码相机的超分辨率处理等领域都有广泛应用。
-Image stitching technology can be widely used in many fields, detection in space, seabed survey, medical, meteorological, geological surveying, military, video compression and transmission of files for digital preservation, video indexing and retrieval of three-dimensional reconstruction of the object, a digital camerathe super-resolution processing has a wide range of applications Platform: |
Size: 8711168 |
Author:Jason |
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Description: Background of Content Based Image Retrieval(CBIR)
Analysis of Previous Work
Results of Existing Methods
Scalable Image Indexing and Retrieval
Proposed Indexing System(overview)
wavelet transform and HSL color conversion
Feature Space(Energy and Color)
Image Key Generation
Scalable Database Structure
Steps Involved in the Indexing Process
Experimental Results
Conclusions and Future Work
Counter Examples
Platform: |
Size: 1363968 |
Author:fia4joy |
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Description: Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve contentbased
face retrieval by constructing semantic codewords for efficient
large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attributeembedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that theproposed methods can achieve up to 43.5 relative improvement in MAP compared to the existing methods.-Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve contentbased
face retrieval by constructing semantic codewords for efficient
large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attributeembedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that theproposed methods can achieve up to 43.5 relative improvement in MAP compared to the existing methods. Platform: |
Size: 18432 |
Author:suhail |
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Description: This paper provides a comprehensive survey of the technical achievements in the
research area of image retri , especially content-based image retri , an area that
has been so active and prosperous in the past few years. The survey includes 100+
papers covering the research aspects of image feature representation and extraction,
multidimensional indexing, and system design, three of the fundamental bases of
content-based image retri . Furthermore, based on the state-of-the-art technology
available now and the demand real-world applications, open research issues are
identified and future promising research directions are suggested-This paper provides a comprehensive survey of the technical achievements in the
research area of image retri , especially content-based image retri , an area that
has been so active and prosperous in the past few years. The survey includes 100+
papers covering the research aspects of image feature representation and extraction,
multidimensional indexing, and system design, three of the fundamental bases of
content-based image retri . Furthermore, based on the state-of-the-art technology
available now and the demand real-world applications, open research issues are
identified and future promising research directions are suggested Platform: |
Size: 92160 |
Author:silkan |
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Description: 最新的几篇图像检索方面的论文。在检索原理上，无论是基于文本的图像检索还是基于内容的图像检索，主要包括三方面：一方面对用户需求的分析和转化，形成可以检索索引数据库的提问；另一方面，收集和加工图像资源，提取特征，分析并进行标引，建立图像的索引数据库；最后一方面是根据相似度算法，计算用户提问与索引数据库中记录的相似度大小，提取出满足阈值的记录作为结果，按照相似度降序的方式输出。-The latest image retri several papers. On a retri principle, whether it is text-based image retri and content-based image retri , mainly includes three aspects: one user needs analysis and transformation, the formation can be retrieved index question on the other hand, the collection and processing of images resources, extracting features, analysis and indexing, create an image of the index and finally the one hand, according to the similarity algorithm to calculate the size of the user question and the similarity index records in the , extract the records that meet the threshold as a result, according to similarity degree descending output. Platform: |
Size: 35961856 |
Author:猫熊 |
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Search - image indexing and retrieval