检索结果-内蒙古大学图书馆

10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Bhowmik, Tapan Kumar Roy, Utpal Parui, Swapan K. Faculty of Mathematics and Natural Sciences University of Groningen Netherlands Department of Computer and System Sciences Visva Bharati Santiniketan India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9780769546612

In this paper we introduce a stroke based lexicon reduction technique in order to reduce the search space for recognition of handwritten words. The principle of this technique involves mainly two aspects of a word image to constitute a feature vector: one is word-length and the other is shape of the word. The length of the word image is represented by the number of specific vertical strokes present in the word image and, on the other hand, the shape of a word image is realized with the combination of both horizontal and vertical strokes. The experiment has been carried out with a database of 35,700 off-line handwritten Bangla word images. Though our proposed lexicon reduction technique is developed for recognition of Bangla handwritten words, its generalization property can easily be exploited for recognition of handwriting in other scripts also. © 2012 IEEE.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

BYOLMED3D: SELF-SUPERVISED REPRESENTATION LEARNING OF MEDICAL VIDEOS USING GRADIENT ACCUMULATION ASSISTED 3D BYOL FRAMEWORK

arXiv

引用

arXiv 2022年

作者： Manna, Siladittya Dey, Rakesh Chakraborty, Souvik Computer Vision and Pattern Recognition Unit Indian Statistical Institue Kolkata India Computer Vision Researcher

Applications on Medical Image Analysis suffer from acute shortage of large volume of data properly annotated by medical experts. Supervised Learning algorithms require a large volumes of balanced data to learn robust representations. Often supervised learning algorithms require various techniques to deal with imbalanced data. Self-supervised learning algorithms on the other hand are robust to imbalance in the data and are capable of learning robust representations. In this work, we train a 3D BYOL self-supervised model using gradient accumulation technique to deal with the large number of samples in a batch generally required in a self-supervised algorithm. To the best of our knowledge, this work is one of the first of its kind in this domain. We compare the results obtained through our experiments in the downstream task of ACL Tear Injury detection with the contemporary self-supervised pre-training methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained weights. From the downstream task experiments, it is evident that the proposed framework outperforms the existing baselines. © 2022, CC0.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Multi-gradient-direction based deep learning model for arecanut disease identification

引用

CAAI Transactions on Intelligence Technology 2022年第2期7卷 156-166页

作者： S.B.Mallikarjuna Palaiahnakote Shivakumara Vijeta Khare M.Basavanna Umapada Pal B.Poornima Department of Computer Science and Engineering Bapuji Institute of Engineering and TechnologyDavanagereAffiliated to Visvesvaraya Theological UniversityBelagaviKarnatakaIndia Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Adani Institute of Infrastructure Engineering AhmedabadIndia Department of Computer Science Davanagere UniversityDavanagereKarnatakaIndia Computer Vision and Pattern Recognition Unit Indian Statistical InstituteKolkataWest BengalIndia

Arecanut disease identification is a challenging problem in the field of image *** this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and *** to the effect of the disease,there are chances of losing vital details in the *** enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced *** proposed method extracts arecanut as foreground from the enhanced images using Otsu ***,the features are extracted for foreground information for disease identification by exploring the ResNet *** advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut *** results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.

关键词： deep learning image analysis pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training

Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...

引用

2023 IEEE/CVF Conference on computer vision and pattern Recognition Workshops, CVPRW 2023

作者： Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phone unlocking, face payment and self-service security inspection. However, further exploration is required to fully secure FAS in long-distance settings. In this paper, we propose two contributions to enhance the security of face recognition systems: Dynamic Feature Queue (DFQ) and Progressive Training Strategy (PTS). DFQ converts the conventional binary classification task into a multi-classification task. It treats live samples as a closed set and attack samples as an open set by using a dynamic queue that stores the features of spoofing samples and updates them. On the other hand, PTS targets difficult samples and iteratively adds them in batches for training. The proposed PTS divides the entire training set into blocks, trains only a small portion of the data, and gradually increases the training data with each stage while also incorporating low-scoring positive samples and high-scoring spoof samples from the test set. These two contributions complement each other by enhancing the model's ability to generalize and defend against various types of attacks, making the face recognition system more secure and reliable. Our proposed methods have achieved top performance on ACER metric with 4.73% on the SuHiFiMask dataset [11] and won the first prize in Surveillance Face Anti-spoofing track of the Challenge@CVPR 2023. © 2023 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Comparative study of Devnagari handwritten character recognition using different feature and classifiers

Comparative study of Devnagari handwritten character recogni...

引用

ICDAR2009 - 10th International Conference on Document Analysis and Recognition

作者： Pal, U. Wakabayashi, T. Kimura, F. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-108 India Graduate School of Engineering Mie University TSU Mie 514-8507 Japan

ISBN: (纸本)9780769537252

In recent years research towards Indian handwritten character recognition is getting increasing attention. Many approaches have been proposed by the researchers towards handwritten Indian character recognition and many recognition systems for isolated handwritten numerals/characters are available in the literature. To get idea of the recognition results of different classifiers and to provide new benchmark for future research, in this paper a comparative study of Devnagari handwritten character recognition using twelve different classifiers and four sets of feature is presented. Projection distance, subspace method, linear discriminant function, support vector machines, modified quadratic discriminant function, mirror image learning, Euclidean distance, nearest neighbour, k-Nearest neighbour, modified projection distance, compound projection distance, and compound modified quadratic discriminant function are used as different classifiers. Feature sets used in the classifiers are computed based on curvature and gradient information obtained from binary as well as gray-scale images. © 2009 IEEE.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Pectoral muscle detection in digital breast tomosynthesis and mammography

Pectoral muscle detection in digital breast tomosynthesis an...

引用

3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Ghesu, Florin C. Wels, Michael Jerebko, Anna Sühling, Michael Hornegger, Joachim Kelm, B. Michael Siemens Corporate Technology Imaging and Computer Vision Erlangen Germany Pattern Recognition Department Friedrich Alexander University Erlangen Germany Siemens AG Healthcare Erlangen Germany

ISBN: (纸本)9783319055299

Screening and diagnosis of breast cancer with Digital Breast Tomosynthesis (DBT) and Mammography are increasingly supported by algorithms for automatic post-processing. The pectoral muscle, which dorsally delineates the breast tissue towards the chest wall, is an important anatomical structure for navigation. Along with the nipple and the skin, the pectoral muscle boundary is often used for reporting the location of breast lesions. It is visible in mediolateral oblique (MLO) views where it is well approximated by a straight line. Here, we propose two machine learning-based algorithms to robustly detect the pectoral muscle in MLO views from DBT and mammography. Embedded into the Marginal Space Learning framework, the algorithms involve the evaluation of multiple candidate boundaries in a hierarchical manner. To this end, we propose a novel method for candidate generation using a Hough-based approach. Experiments were performed on a set of 100 DBT volumes and 95 mammograms from different clinical cases. Our novel combined approach achieves competitive accuracy and robustness. In particular, for the DBT data, we achieve significantly lower deviation angle error and mean distance error than the standard approach. The proposed algorithms run within a few seconds. © 2014 Springer International Publishing Switzerland.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A new method for arbitrarily-oriented text detection in video

A new method for arbitrarily-oriented text detection in vide...

引用

10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Sharma, Nabin Shivakumara, Palaiahnakote Pal, Umapada Blumenstein, Michael Tan, Chew Lim Griffith University QLD Australia School of Computing National University of Singapore Singapore Singapore Computer Vision and Pattern Recognition Unit Indian Statistical Unit Kolkata India

ISBN: (纸本)9780769546612

Text detection in video frames plays a vital role in enhancing the performance of information extraction systems because the text in video frames helps in indexing and retrieving video efficiently and accurately. This paper presents a new method for arbitrarily-oriented text detection in video, based on dominant text pixel selection, text representatives and region growing. The method uses gradient pixel direction and magnitude corresponding to Sobel edge pixels of the input frame to obtain dominant text pixels. Edge components in the Sobel edge map corresponding to dominant text pixels are then extracted and we call them text representatives. We eliminate broken segments of each text representatives to get candidate text representatives. Then the perimeter of candidate text representatives grows along the text direction in the Sobel edge map to group the neighboring text components which we call word patches. The word patches are used for finding the direction of text lines and then the word patches are expanded in the same direction in the Sobel edge map to group the neighboring word patches and to restore missing text information. This results in extraction of arbitrarily-oriented text from the video frame. To evaluate the method, we considered arbitrarily-oriented data, non-horizontal data, horizontal data, Hua's data and ICDAR-2003 competition data (Camera images). The experimental results show that the proposed method outperforms the existing method in terms of recall and f-measure. © 2012 IEEE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Online handwritten Indian script recognition: A human motor function based framework

Online handwritten Indian script recognition: A human motor ...

引用

作者： Garain, U. Chaudhuri, B.B. Pal, T.T. Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata 700 108 India

This paper presents the online handwriting recognition for Indian scripts. The primary concern of the approach is the modeling of human motor functionality while writing characters. This is achieved by looking at the whole pen trajectory where the time evaluation of the pen coordinates plays a crucial role. A low complexity classifier has been designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles. Initially, the approach has been applied for online recognition of handwritten characters in Devnagari and Bangla, the two major Indian scripts. A test on a dataset of considerable size shows promising recognition rates namely, 97.29% for Devnagari and 96.34% for Bangla. © 2002 IEEE.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Off-line Bangla signature verification

Off-line Bangla signature verification

引用

10th IAPR International Workshop on Document Analysis Systems, DAS 2012

作者： Pal, Srikanta Nguyen, Vu Blumenstein, Michael Pal, Umapada School of Information and Communication Technology Griffith University Gold Coast QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata-700108 India

ISBN: (纸本)9780769546612

In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments. © 2012 IEEE.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Texton-based fabric defect detection and recognition

引用

Iranian Journal of Electrical and computer Engineering 2011年第2期10卷 57-69页

作者： Behravan, M. Tajeripour, F. Azimifar, Z. Boostani, R. Computer Vision and Pattern Recognition Group School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Automatic fabric defect classification is an essential stage in production line of textile manufactures. Here, a novel method for simultaneous detection and classification of fabric defects is presented. Each fabric defect type produces a locally specific type of texture. In this paper for the first time textons are used to describe local structure of fabric defects textures. In the method, an image is divided into several blocks in which features based on filter banks are extracted from them. According to these features, the algorithm determines defective windows and their defect types. The employed datasets in this study contain six types of well-known defects of patterned fabrics and four types of unpatterned fabric defects. Our experiments have yielded excellent results and this shows that descriptors based on texture properties of defective regions are suitable for classification task. The proposed method leads to improved and robust detection and classification results compared to the traditional methods. © 2011 ACECR.

关键词： Feature extraction

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：