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4th Workshop on Document Analysis and recognition, DAR 2018, held in Conjunction with the 11th Indian Conference on vision, Graphics, and Image Processing, ICVGIP 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. This paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. Then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. This process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Image segmentation

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Digging into Uncertainty in Self-supervised Multi-view Stereo

Digging into Uncertainty in Self-supervised Multi-view Stere...

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International Conference on computer vision (ICCV)

作者： Hongbin Xu Zhipeng Zhou Yali Wang Wenxiong Kang Baigui Sun Hao Li Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Alibaba Group Pazhou Laboratory Shanghai AI Laboratory

ISBN: (纸本)9781665428132

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (U-MVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the invalid supervision in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework 1 achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents.

关键词： Optical losses Optical filters computer vision Uncertainty Three-dimensional displays Monte Carlo methods Benchmark testing

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 1001 Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 A-1040 Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG A-8010 Graz Austria

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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

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

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Keyao Wang Mouxiao Huang Guosheng Zhang Haixiao Yue Gang Zhang Yu Qiao Department of Computer Vision Technology (VIS) Baidu Inc. ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

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.

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Saliency attention based abnormal event detection in video

Saliency attention based abnormal event detection in video

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IEEE International Conference on Robotics and Biomimetics

作者： Wang Huan Huiwen Guo Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition University of Chinese Academy of Sicences Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781479973989

Most existing methods for abnormal event detection in the literature are relied on a training phase. Different from conventional approaches for abnormal event detection, a saliency attention based abnormal event detection approach is proposed in this paper. It is inspired by the visual attention mechanism that abnormal events are those which attract attention mostly in videos. The temporal and spatial abnormal saliency maps are firstly constructed and then the final abnormal event map is formatted by fusing them using a method with dynamic coefficients. The temporal abnormal saliency map is constructed by motion contrast between keypoints extracted from two successive video frames. The spatial abnormal saliency map is structured based on the color contrasts. Experiments performed on the benchmark datasets show that the proposed method achieves a high accurate and robust results for abnormal event detection without a training phase.

关键词： Clustering algorithms Hidden Markov models Event detection Conferences Color computer vision pattern recognition

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Digging into uncertainty in self-supervised multi-view stereo

arXiv

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arXiv 2021年

作者： Xu, Hongbin Zhou, Zhipeng Wang, Yali Kang, Wenxiong Sun, Baigui Li, Hao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Shanghai AI Laboratory Alibaba Group Pazhou Laboratory

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (UMVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the invalid supervision in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents. © 2021, CC BY.

关键词： Image reconstruction

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Water reservoir based approach for touching numeral segmentation

Water reservoir based approach for touching numeral segmenta...

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International Conference on Document Analysis and recognition

作者： U. Pal A. Belaid C. Choisy Group READ LORIA Campus Scientifique France Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Group READ LORIA Campus Scientifique Vandoeuvre-les-Nancy France

ISBN: (纸本)0769512631

Deals with a scheme for automatic segmentation of unconstrained handwritten connected numerals. The scheme is mainly based on features obtained from a new concept based on a water reservoir. A reservoir is a metaphor to illustrate the region where numerals touch. The reservoir is obtained by considering accumulation of water poured from the top or from the bottom of the numerals. At first, considering the reservoir location and size, touching positions (top, middle and bottom) are decided. Next, by analyzing the reservoir boundary, touching position and topological features of the touching pattern, the best cutting point is determined. Finally, combined with morphological structural features the cutting path for segmentation is generated.

关键词： Water resources Reservoirs Water storage pattern analysis computer vision pattern recognition Handwriting recognition Joining IEEE

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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16th International Conference on pattern recognition, ICPR 2002

作者： Jogan, Matjaž Leonardis, Aleš Wildenauer, Horst Bischof, Horst Faculty of Computer and Information Science University of Ljubljana Tržaška 25 Ljubljana1001 Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9/1832 ViennaA-1040 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 2. OG GrazA-8010 Austria

ISBN: (纸本)0769516963

Methods for mobile robot localization that use eigen spaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigen space. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigen space-based recognition method. © 2002 IEEE.

关键词： Mobile robots

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Unsupervised Feature Learning for Writer Identification and Writer Retrieval

arXiv

引用

arXiv 2017年

作者： Christlein, Vincent Gropp, Martin Fiel, Stefan Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Computer Vision Lab TU Wien Vienna1040 Austria

Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features trained without supervision are superior to descriptors of state-of-the-art writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16). Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization computer vision Histograms Windows

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