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

921 篇 会议
61 册 图书
23 篇 期刊文献

馆藏范围

1,005 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

822 篇 工学
- 719 篇 计算机科学与技术...
- 375 篇 软件工程
- 336 篇 电气工程
- 109 篇 信息与通信工程
- 105 篇 光学工程
- 91 篇 生物工程
- 54 篇 生物医学工程（可授...
- 41 篇 电子科学与技术（可...
- 35 篇 化学工程与技术
- 28 篇 控制科学与工程
- 21 篇 机械工程
- 15 篇 安全科学与工程
- 12 篇 仪器科学与技术
- 10 篇 土木工程
- 8 篇 建筑学
- 8 篇 航空宇航科学与技...
- 7 篇 交通运输工程
281 篇 理学
- 127 篇 物理学
- 118 篇 数学
- 94 篇 生物学
- 39 篇 统计学（可授理学、...
- 33 篇 化学
- 11 篇 系统科学
269 篇 医学
- 269 篇 临床医学
- 25 篇 基础医学(可授医学...
- 22 篇 药学(可授医学、理...
75 篇 管理学
- 63 篇 图书情报与档案管...
- 13 篇 管理科学与工程(可...
- 8 篇 工商管理
10 篇 法学
- 9 篇 社会学
4 篇 农学
3 篇 经济学
1 篇 教育学

主题

46 篇 computer graphic...
44 篇 artificial intel...
42 篇 image processing...
33 篇 computer vision
29 篇 deep learning
27 篇 pattern recognit...
25 篇 image segmentati...
22 篇 computer imaging...
20 篇 image enhancemen...
19 篇 graphics process...
17 篇 face recognition
17 篇 image reconstruc...
15 篇 cameras
13 篇 image processing
11 篇 generative adver...
10 篇 object detection
10 篇 computer communi...
10 篇 pixels
10 篇 feature extracti...
10 篇 machine learning

机构

15 篇 indian institute...
11 篇 indian statistic...
11 篇 carnegie mellon ...
11 篇 hebrew universit...
11 篇 google research
10 篇 indian inst tech...
8 篇 indian stat inst...
8 篇 indian inst tech...
7 篇 indian institute...
7 篇 indian institute...
7 篇 indian inst tech...
7 篇 indian inst tech...
7 篇 technische unive...
7 篇 indian inst sci ...
6 篇 indian institute...
6 篇 indian institute...
6 篇 indian inst tech...
6 篇 technical univer...
6 篇 indian inst tech...
6 篇 department of co...

作者

31 篇 chaudhury santan...
26 篇 mukherjee jayant...
25 篇 chaudhuri subhas...
19 篇 das sukhendu
15 篇 babu r. venkates...
14 篇 das partha prati...
14 篇 lall brejesh
14 篇 raman shanmugana...
13 篇 harit gaurav
12 篇 mukherjee dipti ...
12 篇 chanda bhabatosh
12 篇 mishra deepak
11 篇 martial hebert
11 篇 yair weiss
11 篇 vittorio ferrari
11 篇 cristian sminchi...
11 篇 biswas prabir ku...
10 篇 banerjee biplab
10 篇 biswas soma
10 篇 sur arijit

语言

998 篇 英文
4 篇 中文
3 篇 俄文
2 篇 其他

检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

共 1005 条记录，以下是451-460 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：

Semantic Event Detection and Classification in Cricket Video Sequence

Semantic Event Detection and Classification in Cricket Video...

引用

6th indian conference on computer vision, graphics and image processing

作者： Kolekar, M. H. Palaniappan, K. Sengupta, S. Univ Missouri Dept Comp Sci Columbia MO 65211 USA Indian Inst Technol Dept Elect & ECE Kharagpur W Bengal India

ISBN: (纸本)9781424442195

In this paper we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. the proposed scheme performs a top-down video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify excitement clips into real-time and replay segments. At level-3, we classify these segments into field view and non-field view based on dominant grass color ratio. At level-4a, we classify field view into pitch-view, long-view, and boundary view using motion-mask. At level-4b, we classify non-field view into close-up and crowd using edge density feature. At level-5a, we classify close-ups into the three frequently occurring classes batsman, bowler/fielder umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes spectator and players' gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as highlight extraction, and video skimming.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

computer vision – ECCV 2018 Workshops 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Laura Leal-Taixé Stefan Roth

ISBN: (数字)9783030110246

ISBN: (纸本)9783030110239

the six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European conference on computer vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. the workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.

关键词： image processing and computer vision Artificial Intelligence User Interfaces and Human computer Interaction Special Purpose and Application-Based Systems computer Communication Networks computer graphics

来源：评论

学校读者我要写书评

暂无评论

Nrityantar: Pose oblivious indian classical dance sequence classification system 11

Nrityantar: Pose oblivious Indian classical dance sequence c...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Kaushik, Vinay Mukherjee, Prerana Lall, Brejesh IIT Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781450366151

In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely indian Classical Dance (ICD) classification. the variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task. To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework. the pose signature representation is done in two-fold process. First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNNb-ased parallel single person pose estimator (SPPE). Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression. Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern. thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies. We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.

关键词： Pose Signature Dance classification Action Recognition Motion and Video Analysis Deep Learning Supervised Learning LSTM

来源：评论

学校读者我要写书评

暂无评论

Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences 11

Epipolar Geometry based Learning of Multi-view Depth and Ego...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Prasad, Vignesh Das, Dipanjan Bhowmick, Brojeshwar TCS Res & Amp Innovat Embedded Syst & Amp Robot Kolkata India

ISBN: (纸本)9781450366151

Deep approaches to predict monocular depth and ego-motion have grown in recent years due to their ability to produce dense depth from monocular images. the main idea behind them is to optimize the photometric consistency over image sequences by warping one view into another, similar to direct visual odometry methods. One major drawback is that these methods infer depth from a single view, which might not effectively capture the relation between pixels. Moreover, simply minimizing the photometric loss does not ensure proper pixel correspondences, which is a key factor for accurate depth and pose estimations. In contrast, we propose a 2-view depth network to infer the scene depth from consecutive frames, thereby learning inter-pixel relationships. To ensure better correspondences, thereby better geometric understanding, we propose incorporating epipolar constraints to make the learning more geometrically sound. We use the Essential matrix obtained using Nister's Five Point Algorithm, to enforce meaningful geometric constraints, rather than using it as training labels. this allows us to use lesser no. of trainable parameters compared to state-of-the-art methods. the proposed method results in better depth images and pose estimates, which capture the scene structure and motion in a better way. Such a geometrically constrained learning performs successfully even in cases where simply minimizing the photometric error would fail.

关键词： Monocular Depth Visual Odometry Epipolar Geometry computer vision Unsupervised Learning

来源：评论

学校读者我要写书评

暂无评论

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and Rotation Invariant Character Recognition 11

Scale and Rotation Corrected CNNs (SRC-CNNs) for Scale and R...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Swetha, V. C. Mishra, Deepak Gorthi, Sai Subrahmanyam Indian Inst Space Sci & Technol Thiruvananthapuram Kerala India Indian Inst Technol Tirupati Andhra Pradesh India

ISBN: (纸本)9781450366151

Last decade has witnessed rapid growth for the popularity of Convolutional Neural Networks (CNNs), in detecting and classifying objects. the self trainable nature of CNNs makes them the strongest candidate as a classifier and a feature extractor. However, many of the existing CNN architectures fail recognizing texts or objects under input rotation and scaling. this paper introduces an elegant approach, 'Scale and Rotation Corrected CNN (SRC-CNN)' for scale and rotation invariant text recognition, exploiting the concept of principal component of characters. Prior to training and testing with baseline CNN, 'SRC-CNN' maps each character image to a reference orientation and scale, which is again derived from the character image itself. SRC-CNN is capable of recognizing characters in a document, even though they differ in orientation and scale greatly. the proposed method does not demand any training with samples which are scaled or rotated. the performance of proposed approach is validated on different character data sets like MNIST, MNIST_rot_12k and English alphabets and compared with state of the art rotation invariant classification networks. SRC-CNN is a generalized approach and can be extended for rotation and scale invariant classification of many other datasets as well, choosing any appropriate baseline CNN. Here we have demonstrated the generality of the proposed SRC-CNN on MNIST Fashion data set and found to perform well in rotation and scale invariant classification of objects as well. this paper demonstrates how the basic PCA based rotation and scale invariant image recognition can be integrated to CNN for achieving better rotational and scale invariances in classification.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

computer vision and image processing 1

引用

丛书名： Communications in computer and Information Science

1000年

作者： Neeta Nain Santosh Kumar Vipparthi Balasubramanian Raman

ISBN: (数字)9789811540189

ISBN: (纸本)9789811540172

this two-volume set (CCIS 1147, CCIS 1148) constitutes the refereed proceedings of the 4th International conference on computer vision and image processing. held in Jaipur, India, in September 2019.;the 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. the papers are organized by the topical headings in two parts.;Part I: Biometrics; computer Forensic; computer vision; Dimension Reduction; Healthcare Information Systems; image processing; image segmentation; Information Retrieval; Instance based learning; Machine Learning.;Part II: Neural Network; Object Detection; Object Recognition; Online Handwriting Recognition; Optical Character Recognition; Security and Privacy; Unsupervised Clustering.

关键词： computer Imaging, vision, Pattern Recognition and graphics Machine Learning Information Systems and Communication Service computer Appl. in Social and Behavioral Sciences computer Systems Organization and Communication Networks

来源：评论

学校读者我要写书评

暂无评论

Deep fusion of visual signatures for client-server facial analysis 16

Deep fusion of visual signatures for client-server facial an...

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhattarai, Binod Sharma, Gaurav Jurie, Frederic Normandie Univ UNICAEN ENSICAEN CNRSGREYC Caen France IIT Kanpur Comp Sci & Engn Kanpur Uttar Pradesh India

ISBN: (纸本)9781450347532

Facial analysis is a key technology for enabling humanmachine interaction. In this context, we present a client-server framework, where a client transmits the signature of a face to be analyzed to the server, and, in return, the server sends back various information describing the face e.g. is the person male or female, is she/he bald, does he have a mustache, etc. We assume that a client can compute one (or a combination) of visual features;from very simple and efficient features, like Local Binary Patterns, to more complex and computationally heavy, like Fisher Vectors and CNN based, depending on the computing resources available. the challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance. Our solution is based on learning of a common optimal subspace for aligning the different face features and merging them into a universal signature. We have validated the proposed method on the challenging CelebA dataset, on which our method outperforms existing state-of-art methods when rich representation is available at test time, while giving competitive performance when only simple signatures (like LBP) are available at test time due to resource constraints on the client.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

computer vision – ECCV 2018 Workshops 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Laura Leal-Taixé Stefan Roth

ISBN: (数字)9783030110215

ISBN: (纸本)9783030110208

关键词： image processing and computer vision Artificial Intelligence Special Purpose and Application-Based Systems computer Communication Networks Systems and Data Security computer graphics

来源：评论

学校读者我要写书评

暂无评论

Recognizing Facial Expressions Using Novel Motion Based Features 16

Recognizing Facial Expressions Using Novel Motion Based Feat...

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Mukherjee, Snehasis Vamshi, Bandla Reddy, K. V. Sai Vineeth Kumar Krishna, Repala Vamshi Harish, S. V. S. IIIT Chittoor Sricity India

ISBN: (纸本)9781450347532

this paper introduces two novel motion based features for recognizing human facial expressions. the proposed motion features are applied for recognizing facial expressions from a video sequence. the proposed bag-of-words based scheme represents each frame of a video sequence as a vector depicting local motion patterns during a facial expression. the local motion patterns are captured by an efficient derivation from optical flow. Motion features are clustered and stored as words in a dictionary. We further generate a reduced dictionary by ranking the words based on some ambiguity measure. We prune out the ambiguous words and continue with key words in the reduced dictionary. the ambiguity measure is given by applying a graph-based technique, where each word is represented as a node in the graph. Ambiguity measures are obtained by modelling the frequency of occurrence of the word during the expression. We form expression descriptors for each expression from the reduced dictionary, by applying an efficient kernel. the training of the expression descriptors are made following an adaptive learning technique. We tested the proposed approach with standard dataset. the proposed approach shows better accuracy compared to the state-of-the-art.

关键词： Facial expression Optical flow GWOF WOF Adaptive learning

来源：评论

学校读者我要写书评

暂无评论

image Analysis and processing – ICIAP 2009 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Pasquale Foggia Carlo Sansone Mario Vento

ISBN: (数字)9783642041464

ISBN: (纸本)9783642041457

this book constitutes the refereed proceedings of the 15th International conference on image Analysis and processing, ICIAP 2009, held in Vietri sul Mare, Italy, in September 2009. the 107 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 168 submissions. the papers are organized in topical sections on computer graphics and image processing, low and middle level processing, 2D and 3D segmentation, feature extraction and image analysis, object detection and recognition, video analysis and processing, pattern analysis and classification, learning, graphs and trees, applications, shape analysis, face analysis, medical imaging, and image analysis and pattern recognition.

关键词： image processing and computer vision Pattern Recognition Artificial Intelligence computer graphics Algorithm Analysis and Problem Complexity

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101页 << < 42 43 44 45 46 47 48 49 50 51 > >>

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：