检索结果-内蒙古大学图书馆

International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Sukalpa Chanda Asish Chakrapani GV Anders Brun Anders Hast Umapada Pal David Doermann Department of Information Technology Østfold University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Centre for Image Analysis Uppsala University Sweden Computer Science and Engineering University at Buffalo USA

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.

关键词： Face Face recognition Feature extraction Task analysis Training Adaptation models computer architecture

来源：评论

学校读者我要写书评

暂无评论

Cursive Handwritten Segmentation and recognition for Instructional Videos

Cursive Handwritten Segmentation and Recognition for Instruc...

引用

International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ali Shariq Imran Sukalpa Chanda Faouzi Alaya Cheikh Katrin Franke Umapada Pal Dept. of Computer Science and Media Technology Gjϕvik University College Gjøvik Norway Hogskolen i Gjovik Gjovik Oppland NO Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this paper, we address the issues pertaining to segmentation and recognition of cursive handwritten text from chalkboard lecture videos. Recognizing handwritten text is a challenging problem in instructor-led lecture video. The task gets even tougher with varying handwriting styles and blackboard type. Unlike handwritten text on whiteboard and electronic boards, chalkboard represents serious challenges such as, lack of uniform edge density, weak chalk contrast against blackboard and leftover chalk dust noise as a result of erasing -- and many others. Moreover, the varying color of boards and the illumination changes within the video makes it impossible to use trivial thresholding techniques, for the extraction of content. Many universities throughout the world still heavily rely on chalkboard as a mode of instruction. Therefore, recognizing these lecture content will not only aid in indexing and retrieval applications but will also help understand high level video semantics, useful for Multi-media Learning Objects (MLO). In order to encounter those adversaries, we here propose a system for segmentation and recognition of cursive handwritten text from chalkboard lecture videos. We first create a foreground model to segment background blackboard. We then segment the text characters using one-dimensional vertical histogram. Later, we extract gradient based features and classify those characters using an SVM classifier. We obtained an encouraging accuracy of 86.28% on 5-fold cross validation.

关键词： Videos Feature extraction Text recognition Support vector machines Databases Kernel Handwriting recognition

来源：评论

学校读者我要写书评

暂无评论

Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network

arXiv

引用

arXiv 2022年

作者： Srivastava, Abhishek Tomar, Nikhil Kumar Bagci, Ulas Jha, Debesh Computer Vision and Pattern Recognition Unit Indian Statistical Institute India School of Informatics and Computer Science Indira Gandhi National Open University India Department of Radiology Feinberg School of Medicine Northwestern University United States

Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians’ time for screening, and aid in real-world clinical analysis. computer-Aided diagnosis (CADx) classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and decent accuracy. In this paper, we propose FocalConvNet, a focal modulation network integrated with lightweight convolutional layers for the classification of small bowel anatomical landmarks and luminal findings. FocalConvNet leverages focal modulation to attain global context and allows global-local spatial interactions throughout the forward pass. Moreover, the convolutional block with its intrinsic inductive/learning bias and capacity to extract hierarchical features allows our FocalConvNet to achieve favourable results with high throughput. We compare our FocalConvNet with other state-of-the-art (SOTA) on Kvasir-Capsule, a large-scale VCE dataset with 44,228 frames with 13 classes of different anomalies. Our proposed method achieves the weighted F1-score, recall and Matthews correlation coefficient (MCC) of 0.6734, 0.6373 and 0.2974, respectively outperforming other SOTA methodologies. Furthermore, we report the highest throughput of 148.02 images/second rate to establish the potential of FocalConvNet in a real-time clinical environment. The code of the proposed FocalConvNet is available at https://***/ NoviceMAn-prog/FocalConvNet. © 2022, CC BY.

关键词： Modulation

来源：评论

学校读者我要写书评

暂无评论

More Realistic and Efficient Face-Based Mobile Authentication using CNNs

More Realistic and Efficient Face-Based Mobile Authenticatio...

引用

International Joint Conference on Neural Networks

作者： Abhijit Das Abira Sengupta Muhammad Saqib Umapada Pal Michael Blumenstein Center for Artificial Intelligence School of Software University of Technology Sydney Australia Department of Computer Science Kalyani Government Engineering College Kalyani India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In this work, we propose a more realistic and efficient face-based mobile authentication technique using CNNs. This paper discusses and explores an inevitable problem of using face images for mobile authentication, taken from varying distances with a front/selfie camera of the mobile phone. Incidentally, once an individual comes towards a certain distance from the camera, the face images get large and appear over-sized. Simultaneously sharp features of some portions of the face, such as forehead, cheek, and chin are changed completely. As a result, the face features change and the impact increases exponentially once the individual crosses a certain distance and gradually approaches towards the front camera. This work proposes a solution (achieving better accuracy and facial features, whereby face images were cropped and aligned around its close bounding box) to mitigate the aforementioned identified gap. The work investigated different frontier face detection and recognition techniques to justify the proposed solution. Among all the employed methods evaluated, CNNs worked best. For a quantitative comparison of the proposed method, manually cropped face images/annotations of the face images along with their close boundary were prepared. In turn, we have developed a database considering the above-mentioned scenario for 40 individuals, which will be publicly available for academic research purposes. The experimental results achieved indicate a successful implementation of the proposed method and the performance of the proposed technique is also found to be superior in comparison to the existing state-of-the-art.

关键词： Face Cameras Feature extraction Authentication Face recognition Optical distortion Face detection

来源：评论

学校读者我要写书评

暂无评论

Tamil Handwritten City Name Database Development and recognition for Postal Automation

Tamil Handwritten City Name Database Development and Recogni...

引用

International Conference on Document Analysis and recognition

作者： S. Thadchanamoorthy N.D. Kodikara H.L. Premaretne Umapada Pal Fumitaka Kimura Eastern University Sri Lanka School of Computing University of Colombo Sri Lanka Computer Vision and Pattern Recognition unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

ISBN: (纸本)9781479901937

Although there are some reports on offline Tamil isolated handwritten character recognition, to our knowledge there is only two reports on Tamil off-line handwritten word recognition. Also no city name dataset is available for Tamil script. In this paper we present a Tamil offline city name dataset, we developed, and propose a scheme for recognition. Because of the different writing style of various individuals, some of the characters in a Tamil city name may touch and accurate segmentation of such touching into individual characters is a difficult task. Avoiding proper segmentation here, we consider a city name string as a word and the recognition problem is treated as lexicon driven word recognition. In the proposed method, binarized city names are pre-segmented into primitives (individual character or its parts). Primitive components of each city name are then merged into possible characters to get the best city name using dynamic programming. For merging, total likelihood of characters is used as the objective function and character likelihood is computed based on Modified Quadratic Discriminant Function (MQDF), where direction features are applied. A dataset of 265 Tamil city names is developed. and the database will be available freely to the researchers. From the experiment of the proposed scheme 96.89% city name accuracy is obtained from this dataset.

关键词： Cities and towns Handwriting recognition Accuracy Image segmentation Dynamic programming Feature extraction Cavity resonators

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Skew Detection and Character Segmentation for Handwritten Bangla Words

A Novel Approach to Skew Detection and Character Segmentatio...

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： A. Roy T.K. Bhowmik S.K. Parui U. Roy Department of Computer and System Sciences Visva Bharati University Santiniketan India Embassy Golf Link IBM Global Services Private Limited Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Character segmentation is a necessary preprocessing step for character recognition in many handwritten word recognition systems. The most difficult case in character segmentation is the cursive script. Fully cursive nature of Bangla handwriting, the natural skewness in words poses some challenges for automatic character segmentation. In this article a novel approach to skew detection, correction as well as character segmentation has been presented for handwritten Bangla words as a test case. Segmenting points are extracted on the basis of some patterns observed in the handwritten words. With these segmenting points a graphical path (hereafter referred to as a candidate path) has been constructed. The handwritten words contain some consistent and also inconsistent skewness. Our algorithm can cope with both types of skewness at a time. Further the method is so direct that with the help of a candidate path one can handle both skew correction and segmentation successfully. the algorithm has been tested on a database prepared for laboratory use. The method yields fairly good results for this database.

关键词： Image segmentation Character recognition Optical character recognition software computer vision pattern recognition Testing Databases Algorithm design and analysis Writing Handwriting recognition

来源：评论

学校读者我要写书评

暂无评论

Offline Hand-Written Musical Symbol recognition

Offline Hand-Written Musical Symbol Recognition

引用

International Workshop on Frontiers in Handwriting recognition

作者： Sukalpa Chanda Debleena Das Umapada Pal Fumitaka Kimura Department of Computer Science and Media Technology Gjøvik University College Gjøvik Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University TSU Japan

recognition of offline musical symbols can aid in automatic retrieval of a particular piece of musical notation from a digital repository. Though some work on on-line Musical symbol notations exists, little work has been done on off-line recognition of the symbols. This article proposes a system for offline isolated musical symbol recognition. Efficacy of a texture analysis based feature extraction method is compared with a structural shape descriptor based feature extraction method coupled with a Support Vector Machine (SVM) classifier. Later three different kinds of feature selection techniques were also analyzed to gauge the contribution of each feature in the overall classification process. We compared our results with an existing method and we noted the proposed system exhibited encouraging results and it is better than existing method. The proposed system even worked better when we used MQDF classifier in place of SVM. In a five-fold cross validation experimental framework, considering 3795 music symbols we achieved 97.50% and 98.05% accuracy from SVM and MQDF classifiers, respectively when chain-code histogram features are applied.

关键词： Feature extraction Accuracy Support vector machines Histograms Handwriting recognition Educational institutions Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Trajectory-based scene understanding using dirichlet process mixture model

arXiv

引用

arXiv 2018年

作者： Santhosh, Kelathodi Kumaran Dogra, Debi Prosad Roy, Partha Pratim Chaudhuri, Bidyut Baran School of Electrical Sciences Indian Institute of Technology Bhubaneswar Odisha India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India Techno India University Saltlake city Sector V Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Appropriate modeling of a surveillance scene is essential for detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unusual routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions in road marking road can be avoided. In this paper, we propose an unsupervised and nonparametric method to learn frequently used paths from the tracks of moving objects in Θ(kn) time, where k denotes the number of paths and n represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are considered to make the clustering meaningful using Temporally Incremental Gravity Model (TIGM). In addition, the distance-based scene learning makes it intuitive to estimate the model parameters. Further, we have extended TIGM hierarchically as Dynamically Evolving Model (DEM) to represent notable traffic dynamics of a scene. Experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths (k). We have compared the results with various state-of-the-art methods. We have also highlighted the advantages of the proposed method over existing techniques popularly used for designing traffic monitoring applications. It can be used for administrative decision making to control traffic at junctions or crowded places and generate alarm signals, if necessary. Copyright © 2018, The Authors. All rights reserved.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Bag-of-visual-words for signature-based multi-script document retrieval

arXiv

引用

arXiv 2018年

作者： Mandal, Ranju Roy, Partha Pratim Pal, Umapada Blumenstein, Michael School of Information and Communication Technology Griffith University QLD Australia Dept. of Computer Science & Engineering Indian Institute of Technology Roorkee India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia

An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a Support Vector Machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e. signature strokes) and the background spatial information (i.e. background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The 'Tobacco' [1] document database and an indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents and promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Sclera Segmentation Benchmarking Competition in Cross-resolution Environment

Sclera Segmentation Benchmarking Competition in Cross-resolu...

引用

IAPR International Conference on Biometrics (ICB)

作者： Abhijit Das Umapada Pal Michael Blumenstein Caiyong Wang Yong He Yuhao Zhu Zhenan Sun Inria Sophia Antipolis – Méditerranée France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia Institute of Automation Chinese Academy of Sciences(CASIA)

This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using .a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed. Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：