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arXiv 2023年

作者： Hu, Wenbo Zhan, Hongjian Ma, Xinchen Liu, Cong Yin, Bing Lu, Yue Suen, Ching Y. School of Communication and Electronic Engineering East China Normal University Shanghai200062 China Shanghai Key Laboratory of Multidimensional Information Processing Shanghai200241 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada iFLYTEK Research iFLYTEK Hefei230088 China

In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation. Although existing object detection methods achieve impressive performance on known categories, they struggle to recognize novel symbols without retraining. To address this limitation, we propose a Visually Guided Text Spotting (VGTS) approach that accurately spots novel characters using just one annotated support sample. The core of VGTS is a spatial alignment module consisting of a Dual Spatial Attention (DSA) block and a Geometric Matching (GM) block. The DSA block aims to identify, focus on, and learn discriminative spatial regions in the support and query images, mimicking the human visual spotting process. It first refines the support image by analyzing inter-channel relationships to identify critical areas, and then refines the query image by focusing on informative key points. The GM block, on the other hand, establishes the spatial correspondence between the two images, enabling accurate localization of the target character in the query image. To tackle the example imbalance problem in low-resource spotting tasks, we develop a novel torus loss function that enhances the discriminative power of the embedding space for distance metric learning. To further validate our approach, we introduce a new dataset featuring ancient Dongba hieroglyphics (DBH) associated with the Naxi minority of China. Extensive experiments on the DBH dataset and other public datasets, including EGY, VML-HD, TKH, and NC, show that VGTS consistently surpasses state-of-the-art methods. The proposed framework exhibits great potential for application in historical manuscript text spotting, enabling scholars to efficiently identify and document novel symbols with minimal annotation effort. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Enhanced Boundary Learning for Glass-like Object Segmentation

Enhanced Boundary Learning for Glass-like Object Segmentatio...

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International Conference on Computer Vision (ICCV)

作者： Hao He Xiangtai Li Guangliang Cheng Jianping Shi Yunhai Tong Gaofeng Meng Véronique Prinet LuBin Weng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Key Laboratory of Machine Perception (MOE) Peking University SenseTime Group Research Shanghai AI Lab Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation CAS

ISBN: (纸本)9781665428132

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models will be available for further research.

关键词： Shape Convolution Navigation Semantics Object segmentation Predictive models Robot sensing systems

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Advancing biological super-resolution microscopy through deep learning: a brief review

arXiv

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arXiv 2021年

作者： Yang, Tianjie Luo, Yaoru Ji, Wei Yang, Ge Institute of Biophysics Chinese Academy of Sciences Beijing100101 China College of Life Sciences University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Biology and Machine Intelligence School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatiotemporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in life sciences. However, its technical limitations require trade-offs to balance its spatial resolution, temporal resolution, and light exposure of samples. Recently, deep learning has achieved breakthrough performance in many image processing and computer vision tasks. It has also shown great promise in pushing the performance envelope of super-resolution microscopy. In this brief Review, we survey recent advances in using deep learning to enhance performance of super-resolution microscopy. We focus primarily on how deep learning advances reconstruction of super-resolution images. Related key technical challenges are discussed. Despite the challenges, deep learning is set to play an indispensable and transformative role in the development of super-resolution microscopy. We conclude with an outlook on how deep learning could shape the future of this new generation of light microscopy technology. © 2021, CC BY.

关键词： Image reconstruction

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Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

Probabilistic Inference for Camera Calibration in Light Micr...

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IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Guo Fons J. Verbeek Ge Yang Computational Biology and Machine Intelligence National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LIACS Bioinformatics and Imaging Leiden University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address these issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.

关键词： Cameras Three-dimensional displays Calibration Probabilistic logic Microscopy Solid modeling

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Sequence generation: From both sides to the middle

arXiv

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arXiv 2019年

作者： Zhou, Long Zhang, Jiajun Zong, Chengqing Yu, Heng University of Chinese Academy of Sciences Beijing China National Laboratory of Pattern Recognition CASIA Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China Machine Intelligence Technology Lab Alibaba Group

The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization. Such a framework usually generates a sequence token by token from left to right, hence (1) this autoregressive decoding procedure is time-consuming when the output sentence becomes longer, and (2) it lacks the guidance of future context which is crucial to avoid under-translation. To alleviate these issues, we propose a synchronous bidirectional sequence generation (SBSG) model which predicts its outputs from both sides to the middle simultaneously. In the SBSG model, we enable the left-to-right (L2R) and right-to-left (R2L) generation to help and interact with each other by leveraging interactive bidirectional attention network. Experiments on neural machine translation (En-De, Ch-En, and En-Ro) and text summarization tasks show that the proposed model significantly speeds up decoding while improving the generation quality compared to the autoregressive Transformer. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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Probabilistic inference for camera calibration in light microscopy under circular motion

arXiv

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arXiv 2019年

作者： Guo, Yuanhao Verbeek, Fons J. Yang, Ge Computational Biology and Machine Intelligence Group National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Bioinformatics and Imaging LIACS Leiden University Leiden Netherlands

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address those issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce highfidelity 3D reconstructions and accurate 3D measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability

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Robust Pedestrian detection for semi-automatic construction of a crowded person re-identification dataset 1

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10th International Conference on Articulated Motion and Deformable Objects, AMDO 2018

作者： Huang, Zengxi Feng, Zhen-Hua Yan, Fei Kittler, Josef Wu, Xiao-Jun School of Computer and Software Engineering Xihua University Chengdu China Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Machine Intelligence Jiangnan University Wuxi China

ISBN: (数字)9783319945446

ISBN: (纸本)9783319945439

The problem of re-identification of people in a crowd commonly arises in real application scenarios, yet it has received less attention than it deserves. To facilitate research focusing on this problem, we have embarked on constructing a new person re-identification dataset with many instances of crowded indoor and outdoor scenes. This paper proposes a two-stage robust method for pedestrian detection in a complex crowded background to provide bounding box annotations. The first stage is to generate pedestrian proposals using Faster R-CNN and locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches. We then apply a bottom-up human pose estimation method to detect individual pedestrians in the crowd patches. The locations of all subjects are achieved based on the bounding boxes from the two stages. The identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. The experimental results on a crowded pedestrians dataset demonstrate the effectiveness and efficiency of the proposed method. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Gesture recognition

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LOCALIZE HEAVILY OCCLUDED HUMAN FACES VIA DEEP SEGMENTATION

LOCALIZE HEAVILY OCCLUDED HUMAN FACES VIA DEEP SEGMENTATION

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IEEE International Conference on Image Processing

作者： Kaihao Zhang Yongzhen Huang Ran He Hong Wu Liang Wang Statistical Machine Intelligence & LEarning School of Computer Science & Engineering University of Electronic Science and Technology of China National Laboratory of Pattern Recognition CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467399623

Localizing heavily occluded human faces is a challenging problem in facial detection. Previous methods mainly employ sliding windows by determining whether windows include human faces. In this paper, we provide a novel segmentation-based perspective for heavily occluded face localization with deep convolutional neural networks (CNN). Our model takes an image as input without complicated pre-processing. After several convolutional layers, fully-connected layers and a softmax classifier, we can predict the labels of all pixels in an image, which is the key to localize heavily occluded human faces. Finally, we search a minimal rectangle to localize the human face. Our detector needs neither complex pre-processing nor the time-consuming sliding window. Besides, we use a single model to localize faces to further alleviate computational complexity. Experimental results show that our proposed method is a very effective way to localize heavily occluded human face.

关键词： Facial localization Heavily occluded faces Deep convolutional neural networks Human occluding Imagery (Psychotherapy) images Face sliding windows FENSTER windows windows pixel Graphics input

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Part-based high accuracy recognition of serial numbers in bank notes 6

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6th IAPR TC3 International Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： Feng, Bo-Yuan Ren, Mingwu Zhang, Xu-Yao Suen, Ching Y. School of Computer Science Nanjing University of Science and Technology Nanjing210094 China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing100190 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9783319116556

This paper proposes a novel part-based character recognition method for a new topic of RMB (renminbi bank note, the paper currency used in China) serial number recognition, which is important for reducing financial crime and improving financial market stability and social security. Given an input sample, we first generate a set of local image parts using the Difference-of-Gaussians (DoG) keypoint detector. Then, all of the local parts are classified by an SVM classifier to provide a confidence vector for each part. Finally, three methods are introduced to combine the recognition results of all parts. Since the serial number samples suffer from complex background, occlusion, and degradation, our part-based method takes advantage of both global and local character structure features, and offers an overall increase in robustness and reliability to the entire recognition system. Experiments conducted on a RMB serial number character database show that the test accuracy boosted from 98.90% to 99.33% by utilizing the proposed method with multiple voting based combination strategy. The part-based recognition method can also be extended to other types of banknotes, such as Euro, U.S. and Canadian dollars, or in character recognition applications with complex backgrounds. © Springer International Publishing Switzerland 2014.

关键词： Character recognition

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Extraction of Serial Numbers on Bank Notes

Extraction of Serial Numbers on Bank Notes

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International Conference on Document Analysis and recognition

作者： Bo-Yuan Feng Mingwu Ren Xu-Yao Zhang Ching Y. Suen Department of Computer Science Nanjing University of science and technology National Laboratory of Pattern Recognition (NLPR) Institute of Automation of Chinese Academy of Sciences Centre for Pattern Recognition and Machine Intelligence (CENPAMI) Concordia University

ISBN: (纸本)9781479901937

The study of RMB (renminbi bank note, the paper currency used in China) serial number recognition draws more and more attention in recent years, for reducing financial crime, improving financial market stability and social security. The accuracy of RMB recognition relies heavily on the extraction, which is a challenging problem due to background variations and uneven illumination. In this paper, we present a new system that extracts the RMB characters directly from scanned RMB images. First, two different techniques, namely skew correction and orientation identification are used to detect the region which contains RMB serial number. Then the detected text region is binarized by a combined thresholding technique. After that, a local contrast average method is introduced to extract the RMB characters from the binarization result. The experiments demonstrate that the proposed binarization method outperforms other well-known methods. For character extraction, we report an overlap-recall rate of 79.68% and an overlap-precision rate of 98.10% respectively.

关键词： Image binarization Combination technique Local contrast RMB serial number extraction

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