计算机视觉学位论文

计算机视觉学位论文

没有问题，结论不一样就行用别人同样的思路同样的方法做,只是换一个材料,换一个研究对象,得到不同的结果,这样做工作虽然做不出太好的工作,但是还不算是抄袭。

一、学位要求1、完成学士学位课程学习，获得学士学位；2、完成计算机学硕士学位课程学习，获得计算机学硕士学位；3、参加计算机学硕士学位论文答辩，获得计算机学硕士学位；二、课程要求1、必修课程：计算机科学原理、计算机系统结构、计算机网络原理、计算机软件工程、计算机系统安全、计算机系统维护等；2、选修课程：计算机图形学、计算机视觉、计算机网络安全、计算机系统设计、计算机系统管理、计算机系统维护等；三、论文要求1、撰写计算机学硕士学位论文，内容必须与计算机学硕士学位课程学习有关；2、论文答辩，获得计算机学硕士学位。

计算机视觉期刊

PAMI：IEEE Transactions on Pattern Analysis and Machine Intelligence，IEEE 模式分析与机器智能杂志IJCV：International Journal on Computer Vision，国际计算机视觉杂志 TIP:IEEE Transactions on Image Processing，IEEE图像处理杂志CVIU：Computer Vision and Image Understanding，计算机视觉与图像理解PR：Pattern Recognition，模式识别PRL：Pattern Recognition Letters，模式识别快报

是人工智能领域公认的顶级水平。

TPAMI是计算机视觉和人工智能领域公认的顶级国际期刊，是中国计算机学会（CCF）推荐的A类期刊，也是中国人民大学核心期刊目录中的A+类期刊，影响因子。

2021年1月至今，高瓴人工智能学院已发表或被录用CCF A类期刊和会议论文76篇、CCF B类期刊和论文31篇。

TPAMI是目前计算机类别中影响因子最高（影响因子）的期刊之一，主要收录人工智能、模式识别、计算机视觉及机器学习领域的原创性科研成果。

上海科技大学信息学院智能视觉中心的最新研究成果“Neural Opacity Point Cloud”在人工智能领域顶级学术刊物IEEE TPAMI发表。

TPAMI是模式分析与机器智能IEEE汇刊，中国计算机学会和中国自动化学会等多个学会将其定位为：国际上极少数的顶级刊物，鼓励我国学者去突破。

EEE TPAMI是公认的人工智能、模式识别、图像处理和计算机视觉领域顶级国际期刊，该期刊影响因子（Impact Factor）和谷歌指数（H-Index）在计算机科学和工程技术两个大类学科里均列首位。

同时，该期刊影响因子和谷歌指数列所有计算机学会推荐A类（CCF A类）期刊首位，在计算机科学与人工智能领域具有权威影响力。

计算机视觉论文知乎网

计算机视觉（Computer Vision）是近十几年来计算机科学中最热门的方向之一，而国际计算机视觉与模式识别大会（Conference on Computer Vision and Pattern Recognition，简称CVPR）绝对是计算机视觉会议中的翘楚。1) RGB-D 数据的分析2) 中层patch的分析会是一个热点3) 深度学习以及特征学习也在蓬勃上升时期

天玑学术网Soscholar是由中科院计算机研究所开发的一个分享研究成果、学术著作的交流平台。它以论文检索工具为入口，在这基础上搭建学术社交平台：学者个人微博的构建、学术动态的更新、关注的论文与话题、与其他学者的交流讨论。天玑学术引擎的数据来源涵盖 ACM、IEEE、DBLP、CITESEER，以及众多国外学者的个人博客，目前数据库内的论文储量接近 1000 万。 免费论文搜索网CiteSeerX采用机器自动识别技术搜集网上以Postscrip和PDF文件格式存在的学术论文，然后依照引文索引方法标引和链接每一篇文章。CiteSeerX旨在有效地组织网上文献，多角度促进学术文献的传播与反馈。目前，CiteSeerX存储的文献全文达138万多篇，引文2674万多条，内容主要涉及计算机和信息科学领域，主题包括智能代理、人工智能、硬件、软件工程、操作系统等。 学术论文搜索引擎OCLC是一个不以盈利为目的、提供计算机图书馆服务的会员制研究组织。其宗旨是为广大的用户发展对全世界各种信息的应用以及降低获取信息的成本。 研究者学术搜索Arnetminer是一个可以根据关键词来查找相关的专家、论文和会议、关联关系发现等的平台。目前这个系统是针对计算机学科资源做的知识发现服务。该网站涵盖了100多万名研究者、300万篇论文信息、3700多万引用关系以及8000多个会议信息。ArnetMiner系统在国际顶级会议中得到一致好评，系统数据还被广泛应用于科学研究，是一个在国际上具有一定影响力的文献搜索网站。 文献论文搜索引擎该网站提供涵盖艺术与娱乐、汽车、商业与经融、计算机与技术、健康与健身、新闻与社会、科学教育、体育等各个方面顶极刊物的上千万篇论文。其中大部分为免费全文资料，检索操作简单。它所拥有的文献总量达1100万篇，资料来源于杂志、定期刊物和报纸等。FindArticles是一个在众多搜索引擎包括谷歌上价值很高的学术站点。 学术文献维基百科平台这是一个用户可以自由地编辑优化，评论并添入音频、视频、图片等更多相关文件的平台，属于维基类学术文献百科。 在MiniManuscript上你能看到其他读者在读完某篇文献后整理出来的框架:这篇论文究竟用什么方法研究了什么问题，有了怎样的发现等。有希望成为一个更加开放更有效率的学术平台。 免费学术搜索引擎该网站是由微软联合创始人 Paul Allen 做的免费学术搜索引擎，其检索结果来自于期刊、学术会议资料或者是学术机构的文献。这个搜索引擎能检索到 80% 的免费论文文献，大约有 300 万份，另外它直接提供图表预览，看起来能方便研究人员省下更多筛选的工作。这个搜索引擎覆盖的学科从计算机科学扩展到了脑科学，除了利用作者、出版商标注的关键词这种常用的方式外，还精确到了信息筛选上，例如用计算机视觉等技术搜寻论文发布的会议名称、论文发布的时间，从论文文中筛选出关键词句等，这会让搜索结果的信息看起来更加丰富。 Search 德国比勒菲尔德学术搜索引擎Base是有德国著名的比勒费尔德大学图书馆开发的一个多学科的学术搜索引擎。它提供对全球异构学术资源的集成检索服务。Base整合的文献大约有160个开放资源即超过200万个文档数据信息。来源——知乎@Kangyoung（侵删）

关于计算机视觉的论文

推荐下计算机视觉这个领域，依据学术范标准评价体系得出的近年来最重要的9篇论文吧： （对于英语阅读有困难的同学，访问后可以使用翻译功能） 一、Deep Residual Learning for Image Recognition  摘要：Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. 全文链接： 文献全文 - 学术范 () 二、Very Deep Convolutional Networks for Large-Scale Image Recognition 摘要：In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. 全文链接： 文献全文 - 学术范 () 三、U-Net: Convolutional Networks for Biomedical Image Segmentation 摘要：There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at . 全文链接： 文献全文 - 学术范 () 四、Microsoft COCO: Common Objects in Context 摘要：We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model. 全文链接： 文献全文 - 学术范 () 五、Rethinking the Inception Architecture for Computer Vision 摘要：Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set. 全文链接： 文献全文 - 学术范 () 六、Mask R-CNN 摘要：We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, ., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available. 全文链接： 文献全文 - 学术范 () 七、Feature Pyramid Networks for Object Detection 摘要：Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional networks, partially because they are slow to compute and memory intensive. In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost. A top-down architecture with lateral connections is developed for building high-level semantic feature maps at all scales. This architecture, called a Feature Pyramid Network (FPN), shows significant improvement as a generic feature extractor in several applications. Using a basic Faster R-CNN system, our method achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles, surpassing all existing single-model entries including those from the COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU and thus is a practical and accurate solution to multi-scale object detection. Code will be made publicly available. 全文链接： 文献全文 - 学术范 () 八、ORB: An efficient alternative to SIFT or SURF 摘要：Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone. 全文链接： 文献全文 - 学术范 () 九、DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 摘要：In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First , we highlight convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second , we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third , we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed “DeepLab” system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online. 全文链接： 文献全文 - 学术范 () 希望对你有帮助！

1.《基于深度学习的自然语言处理技术研究》2.《基于深度学习的计算机视觉技术研究》3.《基于深度学习的语音识别技术研究》4.《基于深度学习的机器翻译技术研究》5.《基于深度学习的自动驾驶技术研究》6.《基于深度学习的智能家居技术研究》7.《基于深度学习的智能机器人技术研究》8.《基于深度学习的智能推荐系统技术研究》9.《基于深度学习的自然语言理解技术研究》10.《基于深度学习的智能安全技术研究》

沈雨娇写的论文有撵炉胶，春夜喜雨等论文。沈雨娇的很多偏关于社会学的论文，发表在人才杂志上，引起很大反向。

计算机视觉研究生发论文

数据科学专业的表示NLP需要的训练集太大了，也不好找。只能拿预训练模型针对特殊应用做二次开发，而且对硬件要求很高。图像/视频较NLP来说开放的训练集也好找，而且主题也很多，而且你自己编一个好实现又很实际的商用需求就比较好结题。

空间很大 CV是人工智能的一个分支以后会更加的火爆

这个期刊是国家级核心期刊，A类期刊！！算是 计算机方面最强的几个期刊之一！！

毕业论文是教学科研过程的一个环节，也是学业成绩考核和评定的一种重要方式。毕业论文的目的在于总结学生在校期间的学习成果，培养学生具有综合地创造性地运用所学的全部专业知识和技能解决较为复杂问题的能力并使他们受到科学研究的基本训练。标题标题是文章的眉目。各类文章的标题，样式繁多，但无论是何种形式，总要以全部或不同的侧面体现作者的写作意图、文章的主旨。毕业论文的标题一般分为总标题、副标题、分标题几种。总标题总标题是文章总体内容的体现。常见的写法有：①揭示课题的实质。这种形式的标题，高度概括全文内容，往往就是文章的中心论点。它具有高度的明确性，便于读者把握全文内容的核心。诸如此类的标题很多，也很普遍。如《关于经济体制的模式问题》、《经济中心论》、《县级行政机构改革之我见》等。②提问式。这类标题用设问句的方式，隐去要回答的内容，实际上作者的观点是十分明确的，只不过语意婉转，需要读者加以思考罢了。这种形式的标题因其观点含蓄，轻易激起读者的注重。如《家庭联产承包制就是单干吗?》、《商品经济等同于资本主义经济吗?》等。③交代内容范围。这种形式的标题，从其本身的角度看，看不出作者所指的观点，只是对文章内容的范围做出限定。拟定这种标题，一方面是文章的主要论点难以用一句简短的话加以归纳；另一方面，交代文章内容的范围，可引起同仁读者的注重，以求引起共鸣。这种形式的标题也较普遍。如《试论我国农村的双层经营体制》、《正确处理中心和地方、条条与块块的关系》、《战后西方贸易自由化剖析》等。④用判定句式。这种形式的标题给予全文内容的限定，可伸可缩，具有很大的灵活性。文章研究对象是具体的，面较小，但引申的思想又须有很强的概括性，面较宽。这种从小处着眼，大处着手的标题，有利于科学思维和科学研究的拓展。如《从乡镇企业的兴起看中国农村的希望之光》、《科技进步与农业经济》、《从“劳动创造了美”看美的本质》等。⑤用形象化的语句。如《激励人心的治理体制》、《科技史上的曙光》、《普照之光的理论》等。