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arXiv 2023年

作者： Hao, Xixuan Zhang, Aozhong Meng, Xianze Fu, Bin ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The University of Hong Kong Hong Kong

The goal of text spotting is to perform text detection and recognition in an end-to-end manner. Although the diversity of luminosity and orientation in scene texts has been widely studied, the font diversity and shape variance of the same character are ignored in recent works, since most characters in natural images are rendered in standard fonts. To solve this problem, we present a Chinese Artistic Dataset, termed as ARText, which contains 33, 000 artistic images with rich shape deformation and font diversity. Based on this database, we develop a deformation robust text spotting method (DR TextSpotter) to solve the recognition problem of complex deformation of characters in different fonts. Specifically, we propose a geometric prior module to highlight the important features based on the unsupervised landmark detection sub-network. A graph convolution network is further constructed to fuse the character features and landmark features, and then performs semantic reasoning to enhance the discrimination for different characters. The experiments are conducted on ARText and IC19-ReCTS datasets. Our results demonstrate the effectiveness of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Character recognition

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Orientation Robust Scene Text recognition in Natural Scene*

Orientation Robust Scene Text Recognition in Natural Scene*

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IEEE International Conference on Robotics and Biomimetics

作者： Xiaolong Chen Zhengfu Zhang Yu Qiao Jiangyu Lai Jian Jiang Zeyu Zhang Bin Fu Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences

In recent years, scene text recognition has achieved significant improvement and various state-of-the-art recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multi-orientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multi-oriented nameplates, namely, Orientation Robust Scene Text recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multi-orientation text to near-horizontal text with a carefully designed rectification module. Once the near-horizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90 . 8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0 . 8%. Several extensive experiments have been conducted to verify the effectiveness of our model.

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A semantic model for video based face recognition

A semantic model for video based face recognition

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International Conference on Information and Automation (ICIA)

作者： Dihong Gong Kai Zhu Zhifeng Li Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781479913329

Video-based face recognition has attracted a great deal of attention in recent years due to its wide applications. The challenge of video-based face recognition comes from several aspects. First, video data involves many frames, which increases data size and processing complexity. Second, key frames extracted from videos are usually of high intra-personal discrepancy due to variations in expressions, poses, and illuminations. In order to address these problems, we propose a novel semantic based subspace model to improve the performance of video based face recognition. The basic idea is to construct an appropriate low-dimensional subspace for each person, upon which a semantic model is built to classify the key frames of the person into specific class. After the semantic classification, the key frames belonging to the same classes, i.e. the same semantics, are used to train the linear classifiers for recognition. Extensive experiments on a large face video database (XM2VTS) clearly show that our approach obtains a significant performance improvement over the traditional approaches.

关键词： Face Training Face recognition Semantics Video sequences Principal component analysis Probes

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ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

arXiv

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arXiv 2022年

作者： Mo, Ningkai Gan, Wanshui Yokoya, Naoto Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The University of Tokyo Japan RIKEN Japan

In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RGB-D data using a fully convolutional network, called XYZNet, and directly regresses the 6D pose without any post refinement. In the case of symmetric object, one object has multiple ground-truth poses, and this one-to-many relationship may lead to estimation ambiguity. In order to solve this ambiguity problem, we design a symmetry-invariant pose distance metric, called average (maximum) grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the regression network converge to the correct state, i.e., all minima in the A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on YCB-Video and TLESS datasets demonstrate the proposed framework's substantially superior performance in top accuracy and low computational cost. The relevant code is available in https://***/GANWANSHUI/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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Application of PR techniques to mail sorting in China

Application of PR techniques to mail sorting in China

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2014 7th International C Conference on computer Science and Software Engineering, C3S2E 2014

作者： Liu, Li Lu, Shujing Lu, Yue Suen, Ching Y. Department of Computer Science and Technology East China Normal University Shanghai 200241 China ECNU-SRI Joint Lab. for Pattern Analysis and Intelligent System Shanghai Research Institute of China Post Group Shanghai 200062 China Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal QC H3G 1M8 Canada

ISBN: (纸本)9781450327121

Mail sorting machines play an important role in postal automation. In this paper, we give a brief overview of mail sorting machines in China Post from a pattern recognition point of view. OCR techniques such as postcode recognition and address recognition are essential for mail sorting machines, which are considered as class imbalance problems in our study. In addition to OCR that mainly focuses on the characters in the image, some image-level features are exploited for near-duplicate envelope image matching, which is helpful in tracking pieces of mail in the network environment. © 2014 ACM.

关键词： Image matching

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A Survey of Historical Document Image Datasets

arXiv

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arXiv 2022年

作者： Nikolaidou, Konstantina Seuret, Mathias Mokayed, Hamam Liwicki, Marcus EISLAB Machine Learning Group Luleå University of Technology Aurorum 1 Norrbotten Luleå97187 Sweden Pattern Recognition Lab Computer Vision Group Friedrich-Alexander-Universität Martensstr. 3 Bavaria Erlangen91058 Germany

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies. © 2022, CC BY.

关键词： Deterioration

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Robust real-time tracking for visual surveillance

Eurasip Journal on Advances in Signal Processing

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Eurasip Journal on Advances in Signal Processing 2007年 2007卷

作者： Thirde, David Borg, Mark Aguilera, Josep Wildenauer, Horst Ferryman, James Kampel, Martin School of Systems Engineering Computational Vision Group University of Reading Reading RG6 6AY United Kingdom Computer Science Department Pattern Recognition and Image Processing Group Vienna University of Technology Vienna 1040 Austria

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii)object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints. This integrated approach to complex scene tracking is validated against a number of representative real-world scenarios to show that robust, real-time analysis can be performed. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.

关键词： Tracking (position)

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Active Feature Models

Active Feature Models

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International Conference on pattern recognition

作者： G. Langs P. Peloschek R. Donner M. Reiter H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Department of Radiology Medical University of Vienna Austria

ISBN: (纸本)0769525210

In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical structures that exhibit high texture variation with limited relevance for the recognition of the object location. Experimental results and the comparison to AAMs on different data sets indicate that active feature models can improve search speed and result accuracy, considerably

关键词： Shape Active appearance model Biomedical imaging Image texture Image edge detection Image texture analysis computer graphics pattern recognition Image processing Radiology

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On Moving Object Segmentation from Monocular Video with Transformers

On Moving Object Segmentation from Monocular Video with Tran...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Christian Homeyer Christoph Schnörr Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and *** this paper, we present a novel fusion architecture for monocular motion segmentation - M 3 Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Code will be released upon publication.

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RankSRGAN: Super resolution generative adversarial networks with learning to rank

arXiv

引用

arXiv 2021年

作者： Zhang, Wenlong Liu, Yihao Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Lab Shanghai China

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). To further improve the visual quality of super-resolved results, PIRM2018-SR Challenge employed perceptual metrics to assess the perceptual quality, such as PI, NIQE, and Ma. However, existing methods cannot directly optimize these indifferentiable perceptual metrics, which are shown to be highly correlated with human ratings. To address the problem, we propose Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator in the direction of different perceptual metrics. Specifically, we first train a Ranker which can learn the behaviour of perceptual metrics and then introduce a novel rank-content loss to optimize the perceptual quality. The most appealing part is that the proposed method can combine the strengths of different SR methods to generate better results. Furthermore, we extend our method to multiple Rankers to provide multi-dimension constraints for the generator. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics and quality. Project page: https://***/Projects/RankSRGAN. © 2021, CC BY.

关键词： Generative adversarial networks

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