检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

220 篇 会议
5 篇 期刊文献
2 册 图书

馆藏范围

227 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

217 篇 工学
- 203 篇 计算机科学与技术...
- 25 篇 电气工程
- 20 篇 机械工程
- 4 篇 信息与通信工程
- 4 篇 控制科学与工程
- 4 篇 软件工程
- 1 篇 光学工程
- 1 篇 冶金工程
- 1 篇 建筑学
- 1 篇 土木工程
- 1 篇 生物医学工程（可授...
- 1 篇 生物工程
7 篇 理学
- 2 篇 数学
- 2 篇 物理学
- 1 篇 大气科学
- 1 篇 海洋科学
- 1 篇 生物学
- 1 篇 系统科学
- 1 篇 统计学（可授理学、...
3 篇 医学
- 3 篇 临床医学
2 篇 管理学
- 2 篇 管理科学与工程(可...

主题

44 篇 computer vision
24 篇 pattern recognit...
23 篇 object recogniti...
20 篇 images
15 篇 face recognition
11 篇 object detection
9 篇 action recogniti...
9 篇 training
8 篇 image segmentati...
8 篇 feature extracti...
7 篇 image classifica...
6 篇 cameras
6 篇 gesture recognit...
6 篇 dataset
6 篇 machine learning
6 篇 recognition
5 篇 deep learning
5 篇 visualization
5 篇 image recognitio...
5 篇 shape

机构

6 篇 chinese acad sci...
4 篇 chinese univ hon...
4 篇 mpi intelligent ...
4 篇 carnegie mellon ...
4 篇 univ washington ...
3 篇 univ oxford oxfo...
3 篇 hong kong univ s...
3 篇 mit cambridge ma...
3 篇 univ calif berke...
2 篇 inria
2 篇 stevens inst tec...
2 篇 univ calif river...
2 篇 univ toronto on
2 篇 rensselaer polyt...
2 篇 georgia inst tec...
2 篇 swiss fed inst t...
2 篇 chinese acad sci...
2 篇 chinese acad sci...
2 篇 natl univ singap...
2 篇 swiss fed inst t...

作者

4 篇 hu weiming
3 篇 yuan chunfeng
3 篇 li stan z.
3 篇 lu cewu
3 篇 wen longyin
3 篇 jia jiaya
3 篇 murala subrahman...
3 篇 ji qiang
3 篇 salzmann mathieu
3 篇 shao ling
3 篇 vedaldi andrea
3 篇 yan shuicheng
3 篇 vipparthi santos...
3 篇 brandt jonathan
3 篇 hua gang
2 篇 kannala juho
2 篇 cucchiara rita
2 篇 ling haibin
2 篇 wang limin
2 篇 maji subhransu

语言

227 篇 英文

检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Proceedings of the ieee computer Society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

引用

27th ieee conference on computer vision and pattern recognition, cvpr 2014

ISBN: (纸本)9781479951178

the proceedings contain 539 papers. the topics discussed include: fast and accurate image matching with cascade hashing for 3D reconstruction;minimal solvers for relative pose with a single unknown radial distortion;spectral graph reduction for efficient image and streaming video segmentation;video motion segmentation using new adaptive manifold denoising model;event detection using multi-level relevance labels and multiple features;full-angle quaternions for robustly matching vectors of 3D rotations;semi-supervised spectral clustering for image set classification;learning mid-level filters for person re-identification;DeepReID: deep filter pairing neural network for person re-identification;NMF-KNN: image annotation using weighted multi-view non-negative matrix factorization;beyond comparing image pairs: setwise active learning for relative attributes;and histograms of pattern sets for image classification and object recognition.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Probing Attention-Driven Normalizing Flow Network for Low-Light Image Enhancement 27th

Probing Attention-Driven Normalizing Flow Network for Low-L...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Singh, Siddharth Mehta, Nancy Prakash, K.N. Vipparthi, Santosh Kumar Murala, Subrahmanyam CVPR Lab Indian Institute of Technology Ropar Rupnagar India Vision Lab CAIDAS & IFI University of Wuerzburg Würzburg Germany SR Gudlavalleru Engineering College Vijayawada India CVPR Lab School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9783031781247

Existing low-light image enhancement approaches based upon pixel-wise reconstruction losses are inadept at capturing the complex distribution of well-exposed images, resulting in residual noise, insufficient illuminance, and artifacts. Additionally, the mapping relationship between weakly-illuminated and normally exposed images is one-to-many, making low-light image enhancement a vastly ill-posed problem. In this work, we probe into this one-to-many relationship via an attention and frequency driven normalizing flow network by minimizing the negative log-likelihood loss. the proposed model comprises of two parts: a dual-attention-oriented frequency encoder network and an invertible network which inputs the conditional low-light images and changes the mapping of the complex distribution of well-light images to simpler Gaussian distribution. the proposed model not only utilizes the spatial information inherent in the image for improving the contrast but also extracts the frequency information for preserving the intricate details. To sum up, the distribution of the well-exposed images can be characterized better, and the overall enhancement mechanism becomes analogous to being restrained by a loss function which defines the manifold structure of natural images during the training. Detailed experiment analysis on a variety of challenging low-light images exemplifies the potency of the model and shows its primacy over the state-of-the-art in terms of enhanced quality and efficiency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting 27th

FastTextSpotter: A High-Efficiency Transformer for Multilin...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Das, Alloy Biswas, Sanket Pal, Umapada Lladós, Josep Bhattacharya, Saumik CVPR Unit Indian Statistical Institute Kolkata Kolkata India Computer Vision Center Universitat Autónoma de Barcelona Bellaterra Spain ECE Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9783031784972

the proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficient and robust text spotting solutions. this paper presents FastTextSpotter, a framework that integrates a Swin Transformer visual backbone with a Transformer Encoder-Decoder architecture, enhanced by a novel, faster self-attention unit, SAC2, to improve processing speeds while maintaining accuracy. FastTextSpotter has been validated across multiple datasets, including ICDAR2015 for regular texts and CTW1500 and TotalText for arbitrary-shaped texts, benchmarking against current state-of-the-art models. Our results indicate that FastTextSpotter not only achieves superior accuracy in detecting and recognizing multilingual scene text (English and Vietnamese) but also improves model efficiency, thereby setting new benchmarks in the field. this study underscores the potential of advanced transformer architectures in improving the adaptability and speed of text spotting applications in diverse real-world settings. the dataset, code, and pre-trained models have been released in our Github. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Frequency Modulated Deformable Transformer for Underwater Image Enhancement 27th

Frequency Modulated Deformable Transformer for Underwater I...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Dukre, Adinath Deshmukh, Vivek Kulkarni, Ashutosh Phutke, Shruti Vipparthi, Santosh Kumar Gonde, Anil B. Murala, Subrahmanyam Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India CVPR Lab Indian Institute of Technology Ropar Rupnagar India ETI Lab Yamaha Motor Solutions Faridabad India School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9783031781247

Underwater images frequently experience quality degradation due to refraction, back-scattering, and absorption, leading to color distortion, blurriness, and reduced visibility. Such degradation present in the underwater images can cause inaccuracies while functioning with higher advanced level computer vision applications, equipped for autonomous underwater vehicles. Despite the ability of enhancing the degraded images, existing approaches fail at preserving the localized fine edges also producing the true colors. therefore, an effective pre-processing network is necessary for underwater image enhancement. With this motivation, we propose a frequency modulated deformable transformer network for underwater image enhancement. Initially, the features are extracted with the proposed multi-scale feature fusion feed-forward module. Further, the frequency modulated deformable attention module is proposed to reconstruct fine-level texture in the restored image. Here, we propose a spatio-channel attentive offset extractor in the modulated deformable convolution for focusing on relevant contextual information. Also, adaptive edge-preserving skip connections are proposed for propagating prominent edge features from the network’s shallow layers to its deeper layers. A comprehensive evaluation of the proposed method on synthetic and real-world datasets and extensive ablation analysis demonstrates that the proposed approach shows superior performance than existing state-of-the-art methods. the testing code is provided at https://***/adinathdukre/FMDTUIE. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

Deformable Multi-Scale Network for Snow Removal in Video 27th

Deformable Multi-Scale Network for Snow Removal in Video

引用

27th International conference on pattern recognition, ICPR 2024

作者： He, Runlin Zhou, Gang Xue, Tianhao Liu, Zhaoxi Jia, Zhenhong Key Laboratory of Signal Detection and Processing Department of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (纸本)9783031781247

Snowfall severely degrades outdoor video visibility while reducing the performance of subsequent vision tasks. Although video recovery methods based on deep learning have achieved amazing accomplishments, video snow removal still faces problems such as varying scales and intricate trajectories of snowflakes, which makes it difficult to remove snowflakes and easy to create artifacts on moving objects. To address these issues, we propose a deformable multi-scale video desnowing network. Specifically, we design a multi-scale pseudo-3D residual block(MSRB-P3D) that can effectively remove snowflakes of different scales. Furthermore, a deformable large kernel attention 3Dblock(D-LKA 3Dblock) is introduced to capture the inter-frame dynamic information and reduce the artifacts. Due to the scarcity of dataset, we proposed a new dataset named Synthetic and Real Snowy Video Dataset(SRSVD). Extensive experiments have proven that our proposed method not only outperforms other state-of-the-art methods on both synthetic and real snowy videos, but also effectively improves performance on subsequent vision task. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Snow

来源：评论

学校读者我要写书评

暂无评论

Fusing Image and Text Features for Scene Sentiment Analysis Using Whale-Honey Badger Optimization Algorithm (WHBOA) 27th

Fusing Image and Text Features for Scene Sentiment Analysi...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

ISBN: (纸本)9783031781650

Developing a real-time sentiment analysis application that relies solely on features extracted from images or textual content falls short of capturing human emotions’ nuanced and multifaceted nature. the unlabeled dataset, though useful, has limitations for sentiment analysis due to its general image descriptions, which lack emotional depth and do not include direct sentiment labels. Finding scene sentiment is a challenging task. To address this, combining textual descriptions with visual features is crucial. Important parameters include entropy, bag of words, and parts of speech (nouns, adjectives, and verbs) for textual analysis, alongside visual features like SIFT, SURF, and color histograms. these features are integrated to capture a comprehensive range of sentiment cues, enhancing the accuracy and depth of sentiment insights. this paper proposes an optimized adaptive neuro-fuzzy inference system for a compelling feature enhancement using the Whale-Honey Badger Optimization Algorithm (WHBOA). the proposed method identifies the most relevant and effective features from both textual and visual data. It captures visual-specific attributes to provide a richer and more detailed representation of visual content, addressing the limitations of general image descriptions and paving the way for the development of predictive models. Additionally, text pre-processing cleans and normalizes the textual data. We conducted an extensive comparative performance evaluation to assess the effectiveness and accuracy of the proposed model. the model is compared with the Nearest Neighbor, Support Vector Machine (SVM), and Decision Tree classification algorithms for the performance *** results demonstrate that the optimized model performs better, achieving an accuracy of approximately 91.2%, compared to the other models. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Labeling Cost for High-Accuracy Multi-View Reconstruction 27

Probabilistic Labeling Cost for High-Accuracy Multi-View Rec...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kostrikov, Ilya Horbert, Esther Leibe, Bastian Rhein Westfal TH Aachen Comp Vis Grp Aachen Germany

ISBN: (纸本)9781479951178

In this paper, we propose a novel labeling cost for multi-view reconstruction. Existing approaches use data terms with specific weaknesses that are vulnerable to common challenges, such as low-textured regions or specularities. Our new probabilistic method implicitly discards outliers and can be shown to become more exact the closer we get to the true object surface. Our approach achieves top results among all published methods on the Middlebury DINO SPARSE dataset and also delivers accurate results on several other datasets with widely varying challenges, for which it works in unchanged form.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Noising versus Smoothing for Vertex Identification in Unknown Shapes 27

Noising versus Smoothing for Vertex Identification in Unknow...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Raftopoulos, Konstantinos A. Ferecatu, Marin CNAM Ctr Etud & Rech Informat & Commun CEDRIC FR-75141 Paris 03 France

ISBN: (纸本)9781479951178

A method for identifying shape features of local nature on the shapes boundary, in a way that is facilitated by the presence of noise is presented. the boundary is seen as a real function. A study of a certain distance function reveals, almost counter-intuitively, that vertices can be defined and localized better in the presence of noise, thus the concept of noising, as opposed to smoothing, is conceived and presented. the method works on both smooth and noisy shapes, the presence of noise having an effect of improving on the results of the smoothed version. Experiments with noise and a comparison to state of the art validate the method.

关键词： Vertex Shape Noising Vertex Localization Noise Resistance Shape Representation Object recognition

来源：评论

学校读者我要写书评

暂无评论

A Compositional Model for Low-Dimensional Image Set Representation 27

A Compositional Model for Low-Dimensional Image Set Represen...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Mobahi, Hossein Liu, Ce Freeman, William T. MIT Cambridge MA 02139 USA Microsoft Res Cambridge MA USA

ISBN: (纸本)9781479951178

Learning a low-dimensional representation of images is useful for various applications in graphics and computer vision. Existing solutions either require manually specified landmarks for corresponding points in the images, or are restricted to specific objects or shape deformations. this paper alleviates these limitations by imposing a specific model for generating images;the nested composition of color, shape, and appearance. We show that each component can be approximated by a low-dimensional subspace when the others are factored out. Our formulation allows for efficient learning and experiments show encouraging results.

关键词： images image set point image graphic images computer vision representations

来源：评论

学校读者我要写书评

暂无评论

Transparent Object Reconstruction via Coded Transport of Intensity 27

Transparent Object Reconstruction via Coded Transport of Int...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ma, Chenguang Lin, Xing Suo, Jinli Dai, Qionghai Wetzstein, Gordon Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781479951178

Capturing and understanding visual signals is one of the core interests of computer vision. Much progress has been made w.r.t. many aspects of imaging, but the reconstruction of refractive phenomena, such as turbulence, gas and heat flows, liquids, or transparent solids, has remained a challenging problem. In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation. We show how coded illumination in combination with pairs of recorded images allow for robust computational reconstruction of dynamic two and three-dimensional refractive phenomena.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共23页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：