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

1,413 篇 会议
54 册 图书
27 篇 期刊文献

馆藏范围

1,494 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,010 篇 工学
- 901 篇 计算机科学与技术...
- 410 篇 软件工程
- 389 篇 电气工程
- 131 篇 信息与通信工程
- 122 篇 光学工程
- 90 篇 生物工程
- 65 篇 生物医学工程（可授...
- 58 篇 电子科学与技术（可...
- 38 篇 控制科学与工程
- 34 篇 化学工程与技术
- 29 篇 机械工程
- 21 篇 仪器科学与技术
- 17 篇 安全科学与工程
- 10 篇 材料科学与工程（可...
- 9 篇 建筑学
- 8 篇 土木工程
347 篇 医学
- 344 篇 临床医学
- 27 篇 基础医学(可授医学...
- 26 篇 药学(可授医学、理...
333 篇 理学
- 158 篇 物理学
- 147 篇 数学
- 93 篇 生物学
- 47 篇 统计学（可授理学、...
- 32 篇 化学
- 16 篇 系统科学
92 篇 管理学
- 69 篇 图书情报与档案管...
- 23 篇 管理科学与工程(可...
- 13 篇 工商管理
12 篇 农学
- 12 篇 作物学
10 篇 法学
- 9 篇 社会学
6 篇 经济学
3 篇 教育学
2 篇 文学
1 篇 艺术学

主题

142 篇 computer vision
80 篇 image processing
77 篇 image segmentati...
54 篇 computer graphic...
54 篇 feature extracti...
52 篇 cameras
46 篇 deep learning
38 篇 shape
35 篇 face recognition
34 篇 image reconstruc...
32 篇 artificial intel...
30 篇 robustness
28 篇 object detection
28 篇 image color anal...
25 篇 image enhancemen...
25 篇 image edge detec...
25 篇 pattern recognit...
25 篇 layout
24 篇 biometrics
23 篇 image processing...

机构

20 篇 indian statistic...
19 篇 indian institute...
17 篇 department of co...
14 篇 computer vision ...
12 篇 indian institute...
11 篇 department of el...
10 篇 indian inst tech...
9 篇 indian stat inst...
9 篇 indian institute...
9 篇 indian inst tech...
9 篇 indian institute...
8 篇 indian institute...
8 篇 department of el...
8 篇 indian institute...
8 篇 indian inst sci ...
8 篇 indian inst tech...
7 篇 indian inst tech...
7 篇 indian institute...
7 篇 indian inst tech...
7 篇 indian inst tech...

作者

40 篇 chaudhury santan...
29 篇 chaudhuri subhas...
26 篇 mukherjee jayant...
20 篇 das sukhendu
19 篇 raman shanmugana...
18 篇 santanu chaudhur...
17 篇 balasubramanian ...
16 篇 harit gaurav
16 篇 lall brejesh
15 篇 babu r. venkates...
15 篇 chanda bhabatosh
14 篇 mukherjee dipti ...
13 篇 das partha prati...
12 篇 biswas prabir ku...
12 篇 mishra deepak
11 篇 raman balasubram...
11 篇 sukhendu das
10 篇 biswas soma
10 篇 bhowmick partha
10 篇 bhavsar arnav

语言

1,484 篇 英文
7 篇 中文
3 篇 其他

检索条件"任意字段=Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1494 条记录，以下是1471-1480 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：

A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads✱ 22

A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstr...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Prafful Kumar Khoba Chirag Parikh C.V. Jawahar Ravi Kiran Sarvadevabhatla Rohit Saluja Indian Institute of Technology Delhi IN IIIT Hyderabad IN IIT Mandi IN

ISBN: (纸本)9781450398220

The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets. [GitHub]

关键词： dataset detection gradcam. Fine-grained unconstrained roads

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Deep Network for Group Discovery and Multi-level Activity Recognition 18

Hierarchical Deep Network for Group Discovery and Multi-leve...

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Ashish Goyal Neha Bhargava Subhasis Chaudhuri Rajbabu Velmurugan Indian Institute of Technology Bombay Mumbai Maharashtra

ISBN: (纸本)9781450366151

We present a deep network based hierarchical framework to recognize activities performed by people collectively from videos at various levels of granularity - individual person, group and overall (or scene level). Individual person analysis, which includes person detection, tracking, pose estimation and individual activity recognition, has been studied extensively. Most of the existing work on collective activity recognition has focused on overall scene activity estimation. However, in scenarios where multiple groups perform different group activities, overall scene activity recognition in isolation paints an incomplete picture of various activities. Identifying groups and recognizing their activities is therefore important to understand a scene in it's completeness. To this end, we add an extra layer in existing methods that finds the groups (or clusters) of people present in a scene and their activities. We then utilize these group activities along with the scene context to recognize the scene activity. To discover these groups, we propose a min-max criteria within the framework to train a sub-network which learns pairwise similarity between any two individuals, used by a clustering algorithm for identification of groups. The group activity is captured by an LSTM module whereas the individual and scene activities are captured by CNN-LSTM based modules. These modules along with the grouping layer form the proposed network. We evaluate the network on a publicly available dataset to indicate the usefulness of our approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation 22

Towards the development of personalized and generalized inte...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Shruti Singh Pankaj Pandey Shivam Chaudhary Krishna P Miyapuram James Lomas Indian Institute of Technology Gandhinagar IN Indian Institute of Technology Gandhinagar India TU Delft NL

ISBN: (纸本)9781450398220

Human-computer interaction investigates how people learn from technology, and how they use technology in everyday life. Researchers have used brain-computer interfaces to understand how technology can be designed to support human cognition and behavior. The most famous and consumer-friendly approach to measuring brain signals is electroencephalography (EEG) due to its non-invasive, portable, relatively inexpensive, and high temporal resolution. In this study, we develop machine learning models to distinguish between the neural oscillations of meditators and non-meditators. Previous studies have used power spectrum density, entropy, and functional connectivity to distinguish various meditation traditions. We use EEG data set comprising neural activity of expert meditators of Himalayan Yoga (HYT), Vipassana (VIP), Isha Shoonya (SYN), and non-expert control subjects (CTR). We analyze the data using 13 different machine learning models for within-subject and cross-subject. We present the results for six classification conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series are fed into machine learning classifiers during training. We obtain maximum accuracy for within-subject classification in both meditation and mind-wandering. In cross-subject analysis, we obtained 18.3% above chance level in meditation between control and Isha Shoonya, and similarly above 18% chance level in mind-wandering between control and Vipassana. We discuss the implications of this result for the emerging consumer EEG headset facilitating meditation practice. Our results indicate that personalized models (within-subject) and generalized models (cross-subject) could guide naive (beginner) practitioners to meditate and aim to modulate brain signals by practicing to reach the expert level.

关键词： Brain-computer Interface

来源：评论

学校读者我要写书评

暂无评论

A Temporal Activity Graph Kernel for Human Activity Classification 2018

A Temporal Activity Graph Kernel for Human Activity Classifi...

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Shobhanjana Kalita Arindam Karmakar Shyamanta M. Hazarika Dept of Computer Science & Engg Tezpur University Tezpur Assam Dept of Mechanical Engg IIT Guwahati Guwahati Assam

ISBN: (纸本)9781450366151

Human activities are predominantly spatio-temporal, involving spatial changes over time. Qualitative spatial relations between interacting entities are often used to describe spatial change. To derive such qualitative spatial relations, the interacting entities are approximated as a single bounding box or set of bounding boxes. A set of bounding boxes abstracting a single entity has been termed as an extended object; where each box is bounding a component. Extended object abstraction of spatial entities has been shown to be more effective for representation of human activities [10]. The temporal aspect of an activity is characterized through changing spatial relations between components of interacting extended objects over time. In this paper, we propose Temporal Activity Graph (TAG) based representation model to keep track of the sequences of relations between components of the extended objects. A kernel is designed for classification of spatio-temporal interactions in the TAG based model. The TAG kernel uses concepts of label sequence similarity and interestingness to compute similarity of a pair of TAGs. The TAG kernel is a generic solution that can be used with any kernel based method. Here, the kernel is used within a Support Vector Machine classifier. The TAG kernel based classification of activities is found on par with the state-of-the-art approaches for experiments performed on the Mind's Eye, the UT Interaction, and the SBU Kinect Interaction datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EXPERTNet: Exigent Features Preservative Network for Facial Expression Recognition 2018

EXPERTNet: Exigent Features Preservative Network for Facial ...

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Monu Verma Jaspreet Kaur Bhui Santosh K. Vipparthi Girdhari Singh Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450366151

Facial expressions have essential cues to infer the humans state of mind, that conveys adequate information to understand individuals' actual feelings. Thus, automatic facial expression recognition is an interesting and crucial task to interpret the humans cognitive state through the machine. In this paper, we proposed an Exigent Features Preservative Network (EXPERTNet), to describe the features of the facial expressions. The EXPERTNet extracts only pertinent features and neglect others by using exigent feature (ExFeat) block, mainly comprises of elective layer. Specifically, elective layer selects the desired edge variation features from the previous layer outcomes, which are generated by applying different sized filters as 1 x 1, 3 x 3, 5 x 5 and 7 x 7. Different sized filters aid to elicits both micro and high-level features that enhance the learnability of neurons. ExFeat block preserves the spatial structural information of the facial expression, which allows to discriminate between different classes of facial expressions. Visual representation of the proposed method over different facial expressions shows the learning capability of the neurons of different layers. Experimental and comparative analysis results over four comprehensive datasets: CK+, MMI DISFA and GEMEP-FERA, ensures the better performance of the proposed network as compared to existing networks.

关键词： EXPERTNet Exigent features Feature extraction Facial expression recognition

来源：评论

学校读者我要写书评

暂无评论

End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellation✱ 22

End-to-End GPU-Accelerated Low-Poly Remeshing using Curvatur...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Preetam Chayan Chatterjee Ashutosh Soni Partha Bhowmick, Iit Kharagpur Bhowmick Department of Computer Science and Engineering IIT Kharagpur India Department of Computer Science and Engineering IIT Kharagpur India and ANSYS Software Pvt. Ltd. Hinjewadi India

ISBN: (纸本)9781450398220

We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in GPU. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a compromised face quality. Hence, instead of doing that, we voxelize the input mesh first. On the voxelized surface, we perform curvature estimation and discretization, Centroidal Voronoi Tessellation (CVT), and Delaunay triangulation to generate the output mesh. To control the vertex count, we exploit the curvature map, such that sufficiently large triangles constitute the low-curvature regions, whereas appropriately small triangles build up the high-curvature portions. We produce low-poly meshes at various levels of detail and measure their quality using Hausdorff error. We test our algorithm on different 3D scenes, and they are particularly chosen because they contain multiple objects with varied topologies. Upon testing on these datasets, our algorithm successfully achieves very low-poly approximation with a reasonable mesh quality, and at a reasonably faster pace, thus establishing its efficacy for mesh simplification.

关键词： Surface Voxelization Low-poly Approximation Remeshing GPU-based Algorithms Mesh Simplification Parallel Computing Digital Curvature Estimation Centroidal Voronoi Tessellation

来源：评论

学校读者我要写书评

暂无评论

Longitudinal Analysis of Mask and No-Mask on Child Face Recognition 22

Longitudinal Analysis of Mask and No-Mask on Child Face Reco...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Praveen Kumar Chandaliya Zahid Akhtar Neeta Nain Computer Science and Engineering The LNM Institute of Information Technology Jaipur IN and Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Network and Computer Security College State University of New York Polytechnic Institute US Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450398220

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately, and, respectively.

关键词： Child Face Recognition

来源：评论

学校读者我要写书评

暂无评论

Fuzzy edge detector using entropy optimization

Fuzzy edge detector using entropy optimization

引用

International conference on Information Technology: Coding and Computing (ITCC)

作者： M. Hanmandlu J. See S. Vasikarla Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier f/sub h/ and the crossover point x/sub c/, is used to enhance the image. The entropy function is optimized to obtain the parameters f/sub h/, and x/sub c/ using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, /spl alpha/ and /spl beta/. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors.

关键词： Detectors Entropy image edge detection Fuzzy sets image processing Phase detection Optimization methods Testing image enhancement computer vision

来源：评论

学校读者我要写书评

暂无评论

Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification✱ 22

Contrastive Multi-View Textual-Visual Encoding: Towards One ...

引用

proceedings of the Thirteenth indian conference on computer vision, graphics and image processing

作者： Nakul Sharma Abhirama Subramanyam V B Penamakuri Anand Mishra Indian Institute of Technology Jodhpur IN Indian Institute of Technology Jodhpur IN

ISBN: (纸本)9781450398220

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied ‘closed-set’ and ‘large-scale training samples per category’ logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a ‘very-large-scale’ collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale.

关键词： logo identification. open-set recognition one-shot learning supervised contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Font identification — In context of an Indic script

Font identification — In context of an Indic script

引用

International conference on Pattern Recognition

作者： Sukalpa Chanda Umapada Pal Katrin Franke Department of Computer Science and Media Technology Gjøvik University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India

ISBN: (纸本)9781467322164

Font can be used as a notion of similarity amongst multiple documents written in same script. We could automatically retrieve document images with specific font from a huge digital document repository. So Optical Font Recognition could be a useful pre-processing step in an automated questioned document analysis system for sorting documents with similar fonts. We propose a scheme to identify 10 different fonts for an Indic script (Bangla). Curvature-based features are extracted from segmented characters and are fed to a Support Vector Machine (SVM) classifier. The classifier determines the font type for each segmented character obtained from a document. Later, font identification for that document is executed on the basis of majority voting amongst 10 different fonts for all characters. Using a Multiple Kernel SVM classifier we obtained 98.5% accuracy from 400 test documents (40 documents for each font type).

关键词： Support vector machines Kernel Accuracy Feature extraction Training image segmentation 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150页 << < 141 142 143 144 145 146 147 148 149 150 > >>

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：