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3rd International Conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Almuhajri, Mrouj Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 2W1 Canada Department of Computer Science Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031092817

This work was driven by the possibility of enabling beneficial applications for humans and municipal agencies utilizing deep learning techniques to detect shop signboards. Data is the foundation of successful AI-based applications. Despite the abundance of data, providing datasets for such applications is a challenge as they have to be refined and characterized accurately for better performance. This paper describes the process of collecting the ShoS dataset from Google Street Views for the detection of the signboards of shops on streets. A total of 10k storefront signboards were captured and fully annotated within 7500 images. The ShoS datasets were tested by running different baseline methodology using one-stage detectors YOLO and SSD. Out of the tested methods and variances, the YOLOv3 yields the highest performance reaching 94.23% (mAP@0.5). © 2022, Springer Nature Switzerland AG.

关键词： Object detection

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CCLSL: Combination of Contrastive Learning and Supervised Learning for Handwritten Mathematical Expression recognition 16th

CCLSL: Combination of Contrastive Learning and Supervised ...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Lin, Qiqiang Huang, Xiaonan Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics and Computational Science Sun Yat-Sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510275 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031262838

Handwritten Mathematical Expressions differ considerably from ordinary linear handwritten texts, due to their two-dimentional structures plus many special symbols and characters. Hence, HMER(Handwritten Mathematical Expression recognition) is a lot more challenging compared with normal handwriting recognition. At present, the mainstream offline recognition systems are generally built on deep learning methods, but these methods can hardly cope with HEMR due to the lack of training data. In this paper, we propose an encoder-decoder method combining contrastive learning and supervised learning(CCLSL), whose encoder is trained to learn semantic-invariant features between printed and handwritten characters effectively. CCLSL improves the robustness of the model in handwritten styles. Extensive experiments on CROHME benchmark show that without data enhancement, our model achieves an expression accuracy of 58.07% on CROHME2014, 55.88% on CROHME2016 and 59.63% on CROHME2019, which is much better than all previous state-of-the-art methods. Furthermore, our ensemble model added a boost of 2.5% to 3.4% to the accuracy, achieving the state-of-the-art performance on public CROHME datasets for the first time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Residual and Ensemble Learning on Locally Derived Features for Face recognition 1

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5th International Symposium on Intelligent Systems Technologies and Applications, ISTA 2019

作者： Vinay, A. Gupta, Abhijay Bharadwaj, Aprameya Srinivasan, Arvind Balasubramanya Murthy, K.N. Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University BengaluruKA560085 India

ISBN: (数字)9789811539145

ISBN: (纸本)9789811539138

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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An Encoder-Decoder Approach to Offline Handwritten Mathematical Expression recognition with Residual Attention 3rd

An Encoder-Decoder Approach to Offline Handwritten Mathemati...

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3rd International Conference on pattern recognition and Artificial intelligence, ICPRAI 2022

作者： Lin, Qiqiang Wang, Chunyi Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics Sun Yat-Sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510275 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

Unlike handwritten numeral recognition and handwritten text recognition, the recognition of handwritten mathematical expressions is more difficult because of their complex two-dimensional spatial structure. Since the "watch, attend and parse (WAP)" method was proposed in 2017, encoder-decoder models have made significant progress on handwritten mathematical expression recognition. Our model is improved based on the WAP [1] model. In this paper, the attention module is reasonably added to the encoder to make the extracted features are more informative. The new network is called Dense Attention Network (DATNet), which allows for an adequate extraction of the structural information from handwritten mathematical expressions. To prevent the model from overfitting during the training process, we use label smoothing [2] as the loss. Experiments showed that our model (DATWAP) improved WAP expression recognition from 48.4%/46.8%/45.1% to 54.72%/52.83%/52.54% on the CROHME 2014/2016/2019 test sets. © 2022, Springer Nature Switzerland AG.

关键词： Decoding

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A Real-Time License Plate Detection Method Using a Deep Learning Approach 1

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2nd International Conference on pattern recognition and Artificial intelligence, ICPRAI 2020

作者： Khazaee, Saeed Tourani, Ali Soroori, Sajjad Shahbahrami, Asadollah Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University Montreal Montreal Canada Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

In vision-driven Intelligent Transportation Systems (ITS) where cameras play a vital role, accurate detection and re-identification of vehicles are fundamental demands. Hence, recent approaches have employed a wide range of algorithms to provide the best possible accuracy. These methods commonly generate a vehicle detection model based on its visual appearance features such as license-plate, headlights or some other distinguishable specifications. Among different object detection approaches, Deep Neural Networks (DNNs) have the advantage of magnificent detection accuracy in case a huge amount of training data is provided. In this paper, a robust approach for license-plate detection based on YOLO v.3 is proposed which takes advantage of high detection accuracy and real-time performance. The mentioned approach can detect the license-plate location of vehicles as a general representation of vehicle presence in images. To train the model, a dataset of vehicle images with Iranian license-plates has been generated by the authors and augmented to provide a wider range of data for test and train purposes. It should be mentioned that the proposed method can detect the license-plate area as an indicator of vehicle presence with no Optical Character recognition (OCR) algorithm to distinguish characters inside the license-plate. Experimental results have shown the high performance of the system with precision 0.979 and recall 0.972. © 2020, Springer Nature Switzerland AG.

关键词： Optical character recognition

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Offline Handwritten Mathematical Expression recognition Based on Square Loss Sequence-to-Sequence Neural Network

SSRN

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SSRN 2022年

作者： Wang, Fanglin He, Fukeng Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics Sun Yat-sen University China Tencent China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China Centre for Pattern Recognition and Machine Intelligence Concordia University Canada

Since mathematical expressions play an indispensable role in scientific literature, handwritten mathematical expression recognition (HMER) has recently become an important topic that helps scientists effortlessly use mathematical formulas in their theses and other documents. However, the accuracy of offline handwritten mathematical recognition is currently limited by the ambiguity of mathematical symbols, the complex two-dimensional structure of mathematical formulas and the strong dependence on context. In this paper, we propose a novel offline handwritten mathematical recognition approach based on square loss encoderdecoder neural network (SLSSN). In the proposed neural network, a square loss module, a pre-training module and a statistical language model are added, which can effectively improve the accuracy of offline HMER. Our method’s single model recognition rate exceeds the current best model on the CROHME 2014 test set, and the overall recognition rates for formulas with less than 1, 2 and 3 errors reach high levels of 68.25 %, 76.37 % and 83.16 %, respectively. © 2022, The Authors. All rights reserved.

关键词： Network coding

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Cclsl: Combination of Contrastive Learning and Supervised Learning for Handwritten Mathematical Expression recognition

SSRN

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SSRN 2023年

作者： Lin, Qiqiang Xiao, Haiyang Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics Sun Yat-Sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510275 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

Handwritten Mathematical Expressions differ considerably from ordinary linear handwritten texts, due to their two-dimentional structures plus many special symbols and characters. Hence, HMER(Handwritten Mathematical Expression recognition) is a lot more challenging compared with normal handwriting recognition. At present, the mainstream offline recognition systems are generally built on deep learning methods, but these methods can hardly cope with HEMR due to the lack of training data and diverse writing styles. In this paper, we propose a pre-training framework combining contrastive learning and supervised learning(CCLSL), whose encoder is trained to learn semantic-invariant features between printed and handwritten characters effectively. CCLSL improves the robustness of the model in handwritten styles. Extensive experiments on CROHME benchmark show that without data enhancement, our model achieves an expression accuracy of 58.07% on CROHME2014, 55.88% on CROHME2016 and 59.63% on *** fine-tuning on the handwritten dataset, the accuracy of the model on CROHME2014, CROHME2016, and CROHME2019 reached 59.89%, 56.49%, and 60.13%, which is much better than all previous state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Semantics

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VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts

arXiv

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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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Even big data is not enough: need for a novel reference modelling for forensic document authentication

Even big data is not enough: need for a novel reference mode...

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作者： Garain, Utpal Halder, Biswajit Computer Vision and Pattern Recognition Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute 203 B. T. Road Kolkata700108 India Department of CSE Narula Institute of Technology Agarpara Kolkata700109 India

With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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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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