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

1,385 篇 会议
56 册 图书
27 篇 期刊文献

馆藏范围

1,468 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

998 篇 工学
- 895 篇 计算机科学与技术...
- 398 篇 软件工程
- 390 篇 电气工程
- 125 篇 信息与通信工程
- 119 篇 光学工程
- 84 篇 生物工程
- 62 篇 生物医学工程（可授...
- 56 篇 电子科学与技术（可...
- 37 篇 控制科学与工程
- 33 篇 化学工程与技术
- 27 篇 机械工程
- 17 篇 安全科学与工程
- 16 篇 仪器科学与技术
- 9 篇 材料科学与工程（可...
- 8 篇 建筑学
- 8 篇 土木工程
352 篇 医学
- 349 篇 临床医学
- 26 篇 基础医学(可授医学...
- 25 篇 药学(可授医学、理...
327 篇 理学
- 156 篇 物理学
- 144 篇 数学
- 87 篇 生物学
- 44 篇 统计学（可授理学、...
- 31 篇 化学
- 15 篇 系统科学
87 篇 管理学
- 65 篇 图书情报与档案管...
- 21 篇 管理科学与工程(可...
- 12 篇 工商管理
11 篇 农学
- 11 篇 作物学
10 篇 法学
- 9 篇 社会学
5 篇 经济学
1 篇 教育学
1 篇 文学
1 篇 艺术学

主题

125 篇 computer vision
74 篇 image segmentati...
68 篇 image processing
52 篇 computer graphic...
51 篇 feature extracti...
45 篇 cameras
42 篇 deep learning
35 篇 face recognition
35 篇 shape
33 篇 artificial intel...
30 篇 image reconstruc...
29 篇 object detection
29 篇 robustness
28 篇 image color anal...
26 篇 image enhancemen...
24 篇 image processing...
23 篇 image edge detec...
23 篇 pattern recognit...
23 篇 biometrics
22 篇 layout

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

1,457 篇 英文
8 篇 中文
3 篇 其他

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

共 1468 条记录，以下是271-280 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 Bag of Constrained Visual Words Model for image Representation 3rd

A Bag of Constrained Visual Words Model for Image Representa...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Mukherjee, Anindita Sil, Jaya Chowdhury, Ananda S. Dream Inst Technol Kolkata India IIEST Sibpur Howrah India Jadavpur Univ Kolkata 700032 India

ISBN: (纸本)9789813292918;9789813292901

We propose a bag of constrained visual words model for image representation. Each image under this model is considered to be an aggregation of patches. SURF features are used to describe each patch. Two sets of constraints, namely, the must-link and the cannot-link, are developed for each patch in a completely unsupervised manner. The constraints are formulated using the distance information among different patches as well as statistical analysis of the entire patch data. All the patches from the image set under consideration are then quantized using the Linear-time-Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained k-means algorithm. The resulting clusters, which we term as constrained visual words, are then used to label the patches in the images. In this way, we model an image as a bag (histogram) of constrained visual words and then show its utility for image retrieval. Clustering as well as initial retrieval results on COIL-100 dataset indicate the merit of our approach.

关键词： image representation Constrained visual words LCVQE image retrieval

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Prostate Cancer with Support Vector Machine Using Multiwavelength Photoacoustic images 3rd

Diagnosis of Prostate Cancer with Support Vector Machine Usi...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Borkar, Aniket Sinha, Saugata Dhengre, Nikhil Chinni, Bhargava Dogra, Vikram Rao, Navalgund Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India Univ Rochester Med Ctr Dept Imaging Sci Rochester NY 14642 USA

ISBN: (纸本)9789813290884;9789813290877

Photoacoustic (PA) imaging is an emerging soft tissue imaging modality which can be potentially used for the detection of prostate cancer. computer-aided diagnosis tools help in further enhancing the detection process by assisting the physiologist in the interpretation of medical data. In this study, we aim to classify the malignant and nonmalignant prostate tissue using a support vector machine algorithm applied to the multiwavelength PA data obtained from human patients. The performance comparison between two feature sets, one consisting of multiwavelength PA image pixel values and the other consisting of chromophore concentration values are reported. While chromophore concentration values detected malignant prostate cancer more efficiently, the PA image pixels detected the nonmalignant prostate specimens with higher accuracy. This study shows that multiwavelength PA image data can be efficiently used with the support vector machine algorithm for prostate cancer detection.

关键词： Photoacoustic imaging Prostate cancer Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Cosaliency Detection in images Using Structured Matrix Decomposition and Objectness Prior 3rd

Cosaliency Detection in Images Using Structured Matrix Decom...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Bardhan, Sayanti Jacob, Shibu Indian Inst Technol Madras Dept Comp Sci & Engn Visualizat & Percept Lab Chennai Tamil Nadu India Natl Inst Ocean Technol Marine Sensor Syst Chennai Tamil Nadu India

ISBN: (纸本)9789813290884;9789813290877

Cosaliency detection methods typically fail to perform in the situation where the foreground has multiple components. This paper proposes a novel framework, Cosaliency via Structured Matrix Decomposition (CSMD), for efficient detection of cosalient objects. This paper addresses the issue of saliency detection for images with multiple components in the foreground, by proposing a superpixel level, objectness prior. In this paper, we further propose a novel fusion technique to combine objectness prior to the cosaliency object detection framework. The proposed model is evaluated on challenging benchmark cosaliency dataset that has multiple components in the foreground. It outperforms prominent state-of-the-art methods in terms of efficiency.

关键词： Cosaliency detection Matrix decomposition Fusion Objectness

来源：评论

学校读者我要写书评

暂无评论

CARTOONNET: Caricature Recognition of Public Figures 3rd

CARTOONNET: Caricature Recognition of Public Figures

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Shukla, Pushkar Gupta, Tanu Singh, Priyanka Raman, Balasubramanian Univ Calif Santa Barbara Santa Barbara CA 93106 USA Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

Recognizing faces in the cartoon domain is a challenging problem since the facial features of cartoon caricatures of the same class vary a lot from each other. The aim of this project is to develop a system for recognizing cartoon caricatures of public figures. The proposed approach is based on the Deep Convolutional Neural Networks (DCNN) for extracting representations. The model is trained on both real and cartoon domain representations of a given public figure, in order to compensate the variations in the same class. The IIIT-CFW (Mishra et al., European conference on computer vision, 2016) [1] dataset, which includes caricatures of public figures, is used for the experiments. It is seen from these experiments that improving the performance of the model can be achieved when it is trained on representations from both real and cartoon images of the given public figure. For a total of 86 different classes, an overall accuracy of 79.65% is achieved with this model.

关键词： Face recognition Cartoon recognition Deep Learning Convolutional Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Cell Extraction and Horizontal-Scale Correction in Structured Documents 3rd

Cell Extraction and Horizontal-Scale Correction in Structure...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Srivastava, Divya Harit, Gaurav Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Preprocessing techniques form an important task in document image analysis. In structured documents like forms, cheques, etc., there is a predefined space called frame field/cell for the user to fill the entry. When the user is writing, the nonuniformity of inter-character spacing becomes an issue. Many times, the starting characters of the word are written with sparse spacing between the characters and then gradually with a more compact spacing so as to accommodate the word within the frame field. To deal with this variation in intra-word spacing, horizontal-scale correction is applied to the extracted form fields. The effectiveness of the system is proved by applying it as a preprocessing step in a recognition system proposed in (Almazan et al. in Pattern Anal Mach Intell 36(12):21552-2566, 2014 [2]). The recognition framework results in reduced error rates with this normalization.

关键词： Region growing Cell extraction Horizontal-scale correction Bilingual forms

来源：评论

学校读者我要写书评

暂无评论

Agriculture Parcel Boundary Detection from Remotely Sensed images 3rd

Agriculture Parcel Boundary Detection from Remotely Sensed I...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Khadanga, Ganesh Jain, Kamal Indian Inst Technol Roorkee Civil Engn Dept Geomat Grp Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

The object-based image analysis (OBIA) is extensively used nowadays for classification of high-resolution satellite images (HRSI). In OBIA, the analysis is based on a group of pixels known as objects. It differs from the traditional pixels-based methodology, where individual pixels are analyzed. In OBIA, the image analysis consists of image segmentation, object attribution, and classification. The segmentation process thus identifies a group of pixels and are known as objects. These objects are taken for further analysis. Thus segmentation is an important step in OBIA. In order to find out the boundary of agriculture parcels, a two-step process is followed. First, the segmentation of the images is done using the statistical region merging (SRM) technique. Then the boundary information and center of the segmentation are found out using MATLAB. The best fit segment was found out using trial and errors. The extracted boundary information is very encouraging and it matches the parcel boundaries recorded in revenue registers. The completeness and precision analysis of the plots are also quite satisfactory.

关键词： MATLAB Segmentation Cadastral parcel OBIA HRSI

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial image 3rd

Deep Learning Framework for Detection of an Illicit Drug Abu...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Gupta, Tanu Goyal, Meghna Kumar, Goutam Raman, Balasubramanian Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

ISBN: (纸本)9789813290884;9789813290877

The detection of an illicit drug abuser by analyzing the subject's facial image has been an active topic in the field of machine learning research. The big question here is up to what extent and with what accuracy can a computer model help us to identify if a person is an illicit drug abuser only by analyzing the subject's facial image. The main objective of this paper is to propose a framework which can identify an illicit drug abuser just by giving an image of the subject as an input. The paper proposes a framework which relies on Deep Convolutional Neural Network (DCNN) in combination with Support Vector Machine (SVM) classifier for detecting an illicit drug abuser's face. We have created dataset consisting of 221 illicit drug abusers' facial images which present various expressions, aging effects, and orientations. We have taken random 221 non-abusers' facial images from available dataset named as, Labeled Faces in the Wild (LFW). The experiments are performed using both datasets to attain the objective. The proposed model can predict if the person in an image is an illicit drug abuser or not with an accuracy of 98.5%. The final results show the importance of the proposed model by comparing the accuracies obtained in the experiments performed.

关键词： Deep learning Convolutional neural network Face recognition

来源：评论

学校读者我要写书评

暂无评论

Faster RCNN-CNN-Based Joint Model for Bird Part Localization in images 3rd

Faster RCNN-CNN-Based Joint Model for Bird Part Localization...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Pankajakshan, Arjun Bhavsar, Arnav Indian Inst Technol Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9789813292918;9789813292901

Bird species classification is a challenging task in the field of computer vision because of its fine-grained nature, which in turn can lead to high interclass similarities. An important aspect form any fine-grained categorization problems involves processing of local-level semantics. This highlights the need for accurate part detection/localization. In this work, we propose a two-step approach to address the problem of bird part localization from an input image. In the first step, a Faster RCNN (FRCNN) is learnt to suggest possible bird part regions. However, the part region proposals given by Faster RCNN are not always precise. To refine these, a second step involving a CNN-based part classifier, trained only on bird part segments is used. Both FRCNN and CNN part classifiers are trained separately in a supervised manner. The part classifier effectively builds upon the FRCNN region proposals, as it is trained on more specific data as compared to FRCNN. We evaluate the proposed framework on the standard CUB-200-2011 bird dataset, as well as on a newly collected IIT Mandi bird dataset, where the latter is used only during testing.

关键词： Fine-grained classification Bird part detection Faster RCNN CNN

来源：评论

学校读者我要写书评

暂无评论

Storm Tracking Using Geostationary Lightning Observation Videos 3rd

Storm Tracking Using Geostationary Lightning Observation Vid...

引用

3rd International conference on computer vision and image processing (CVIP)

作者： Joby, Nora Elizabeth George, Nimisha Susan Geethasree, M. N. NimmiKrishna, B. Thayyil, Noora Rahman Sankaran, Praveen Natl Inst Technol Calicut Kerala India

ISBN: (纸本)9789813290884;9789813290877

It was recently observed and proved by geoscientists that lightning observations from space peaked as a precursor to severe weather occurrences like flash floods, cloudbursts, tornadoes, etc. Thus, total lightning observations from space may be used to track such disasters well in advance. Satellite-based tracking is especially important in data-sparse regions (like the oceans) where the deployment of ground-based sensors is unfeasible. The Geostationary Lightning Mapper (GLM) launched in NASA's GOES-R satellite which maps lightning by near-infrared optical transient detection is the first lightning mapper launched in a geostationary orbit. Sample time-lapse videos of these total lightning observations have been published by the GOES-R team. This work describes the challenges, optimizations and algorithms used in the application of tracking filters like the Kalman filter and particle filter for tracking lightning cells and hence storms using these videos.

关键词： Storm track Geostationary lightning mapper Satellite video tracking Disaster prediction Lightning Remote sensing computer vision

来源：评论

学校读者我要写书评

暂无评论

Selective mixing and voting network for semi-supervised domain generalization 21

Selective mixing and voting network for semi-supervised doma...

引用

12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Arfeen, Ahmad Dutta, Titir Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain, by leveraging training samples from multiple source domains. Currently, the training process of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios, where collecting and annotating a large dataset is an expensive and difficult task. Thus, in this paper, we address the problem of Semi-supervised Domain Generalization (SSDG), where the training set contains only a few labeled data, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models, since their performance degrades under such condition. To address this scenario, we propose a novel Selective Mixing and Voting Network (SMV-Net), which effectively extracts useful knowledge from the set of unlabeled training data, available to the model. Specifically, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data, which generalizes effectively across any unseen domain. Secondly, we also propose a voting module, which not only improves the generalization capability of the classifier, but can also comment on the prediction of the test samples, using references from a few labeled training examples, despite of their domain gap. Finally, we introduce a test-time mixing strategy to re-look at the top class-predictions and re-order them if required to further boost the classification performance. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework. © 2021 ACM.

关键词： Large dataset

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147页 << < 24 25 26 27 28 29 30 31 32 33 > >>

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：