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Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-End Handwritten Document Recognition 17th

Faster DAN: Multi-target Queries with Document Positional En...

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17th international conference on Document analysis and Recognition (ICDAR)

作者： Coquenet, Denis Chatelain, Clement Paquet, Thierry Conservatoire Natl Arts & Metiers CEDRIC Paris France LITIS Lab EA 4108 Rouen France Rouen Univ Mont St Aignan France INSA Rouen St Etienne Du Rouvray France

ISBN: (纸本)9783031416842;9783031416859

Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) [9] recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document. However, this autoregressive process leads to inference that cannot benefit from any parallelization optimization. In this paper, we propose Faster DAN, a two-step strategy to speed up the recognition process at prediction time: the model predicts the first character of each text line in the document, and then completes all the text lines in parallel through multi-target queries and a specific document positional encoding scheme. Faster DAN reaches competitive results compared to standard DAN, while being at least 4 times faster on whole single-page and double-page images of the RIMES 2009, READ 2016 and MAUR-DOR datasets. Source code and trained model weights are available at https://***/FactoDeepLearning/FasterDAN.

关键词： Handwritten Document Recognition Document Layout analysis Handwritten Text Recognition Transformer

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A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021

作者： An, Chengyang Chen, Huai Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Radiomics analysis can help patients suffered from head and neck (H&N) cancer customize tailoring treatments. It requires a large number of segmentation of the H&N tumor area in PET and CT images. However, the cost of manual segmentation is extremely high. In this paper, we propose a coarse-to-fine framework to segment the H&N tumor automatically in FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) images. Specifically, we trained three 3D-UNets with residual blocks to make coarse stage, fine stage and refined stage predictions respectively. Experiments show that such a training framework can improve the segmentation quality step by step. We evaluated our framework with Dice Similarity Coefficient (DSC) and Hausdorff Distance at 95% (HD95) of 0.7733 and 3.0882 respectively in the task 1 of the HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR2021) Challenge and ranked second. © 2022, Springer Nature Switzerland AG.

关键词： Positron emission tomography

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance

Application of the Semantic Segmentation Method in the Asses...

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international conference on Management of Large-Scale System Development (MLSD)

作者： Inna Tulokhonova Svetlana Mikhaylova Soelma Danilova Department of Software Engineering and Artificial Intelligence East Siberia State University of Technology and Management Ulan-Ude Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition.

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Computer Aided Detection of Breast Cancer Using Bio Inspired Algorithm

Computer Aided Detection of Breast Cancer Using Bio Inspired...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Syed Rizwana Peddi Kavya Bolla Vinay Pooja Bandi Poojitha Shaik Khaja Mohiddin Basha Dodda Venkatareddy Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopet India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Breast cancer still ranks among the most common causes of cancer-related deaths among women, hence the call for early diagnosis. Mammography is the most accepted screening test, but conventional computer-aided detection (CAD) has a high false positive rate (FPR) that gives rise to biopsy and false negatives (FN) where cancer is undetected. In solving these challenges, this paper provides a solution by employing the use of the Simple Genetic Algorithm (SGA), which is openly inspired by biological systems to enhance the performance of CAD systems for breast cancer detection. The SGA, which is based on the evolutionary process, can resolve problems in feature selection and classification o f t he m ammogram b y overcoming shortcomings of pattern recognition. By mimicking the genetic evolution process, ant colony optimization, and swarm intelligence, the SGA prevents noisy or variant images from anyhow decreasing the detection accuracy. Comprehensive tests on typical sets of mammograms confirm t he e ffectiveness o f t he proposed approach regarding a twofold reduction of inappropriate positive and negative results. This enhanced accuracy of diagnoses can help radiologists to act early, combined with favorable outcomes for the patients, implying that early diagnosis may save lives.

关键词： Technological innovation Accuracy Image analysis Design automation Anxiety disorders Feature extraction Breast cancer Convolutional neural networks Medical diagnostic imaging Genetic algorithms

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Improved ConvNeXt Facial Expression Recognition Embedded with Attention Mechanism 1st

Improved ConvNeXt Facial Expression Recognition Embedded wit...

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1st international conference on Applied intelligence, ICAI 2023

作者： Zhao, Yiteng Ge, Lina Cui, Gaoxiang Fang, Teng School of Artificial Intelligence Guangxi Minzu University Nanning530000 China Key Laboratory of Network Communication Engineering Guangxi Minzu University Nanning530000 China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Nanning530000 China

ISBN: (纸本)9789819709021

Facial expression recognition (FER) is an emerging and important research field in the field of pattern recognition, with wide applications in safe driving, intelligent monitoring, and human-computer interaction. This article addresses the problems of insufficient key information extraction, low recognition accuracy, and easy overfitting in facial expression recognition, and proposes an ECA-ConvNeXt network based on transfer learning strategy and channel attention mechanism. Firstly, the weights of the pre-trained model are initialized using transfer learning on the FER 2013 dataset. Secondly, a series of data augmentation operations are performed on the facial images, allowing them to pass through the ECA-Net attention module of the network, enhancing the key information of the feature regions with high relevance to expressions and suppressing the interference of irrelevant regions in the feature maps. Finally, the inverse bottleneck layer, maximum pooling layer, global average pooling layer, and classification layer are sequentially passed into the network to accelerate the convergence speed and improve the expression recognition rate. Compared to the baseline network, the improved network achieved an accuracy of 72.86%, a recall rate of 72.04%, and a specificity of 64.15% on the FER 2013 dataset. Compared to the commonly used ResNet network and its improvement methods, the proposed ECA-ConvNeXt in this article achieved a 0.19% improvement in recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Tackling the Infodemic: analysis Using Transformer Based Models 1

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1st international Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation, CONSTRAINT 2021 co-located with 35th AAAI conference on Artificial intelligence, AAAI 2021

作者： Zutshi, Anand Raj, Aman Netaji Subhas Institute of Technology Delhi India

ISBN: (数字)9783030736965

ISBN: (纸本)9783030736958

This paper presents how we tackled the COVID 19 Fake News Detection in English subtask in the SHARED TASK@ CONSTRAINT 2021 using RoBERTa. We perform extensive analysis to understand the pattern of the data distribution. To achieve an F1 score of 0.96, we incorporate external sources of misinformation and fine tune multiple state of the art pretrained deep learning models. In the end, we visualise the true and false positives predicted by our model as improvement in future work. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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Harnessing Sustainable Development in Image Recognition Through No-Code AI Applications: A Comparative analysis 4th

Harnessing Sustainable Development in Image Recognition Thro...

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4th international conference on Recent Trends in Image Processing and pattern Recognition, RTIP2R 2021

作者： Kling, Nico Runte, Chantal Kabiraj, Sajal Schumann, Christian-Andreas West Saxon University of Applied Sciences of Zwickau Kornmarkt 1 Zwickau08056 Germany Häme University of Applied Sciences Vankanlähde 9 Hämeenlinna13100 Finland

ISBN: (纸本)9783031070044

Artificial intelligence (AI) solutions and sustainable development have increasingly received public attention and research interest. In this study, the authors discuss the emerging trend of no-code AI solutions, with regards to their contribution to achieving the Sustainable Development Goals, specifically goal 8 (decent work and economic growth) and goal 10 (reduced inequalities). To demonstrate the opportunities that no-code AI may facilitate, the authors compare the performance of conventionally coded models with a no-code model created in Microsoft Lobe, based on secondary data from a dataset offered by Kermany et al. [1] of chest x-rays for pneumonia detection. A total of 5840 JPEG images is used for training and testing, 1575 for normal and 4265 for pneumonia, respectively. Results indicate that the output generated by the studied no-code solution can keep up with coded ones, and partly even outperform them. Possible applications for industries and society include usability cases beyond image recognition, the application in citizen science as well as the exploration of economic development opportunities of no-code AI. Finally, no-code AI could perhaps offer an alternative and emerge as an industry best practice for delivering efficient low-cost solutions in emerging markets, where demographic data is scattered across various homogenous groups. © 2022, Springer Nature Switzerland AG.

关键词： Sustainable development

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Natural Scene Facial Expression Recognition with Dimension Reduction Network

Natural Scene Facial Expression Recognition with Dimension R...

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IEEE international conference on robotics and Automation (ICRA)

作者： Hu, Shenhua Hu, Yiming Li, Jianquan Long, Xianlei Chen, Mengjuan Gu, Qingyi Chinese Acad Sci Res Ctr Precis Sensing & Control Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728173955

As an external manifestation of human emotions, expression recognition plays an important role in human-computer interaction. Although existing expression recognition methods performs perfectly on constrained frontal faces, there are still many challenges in expression recognition in natural scenes due to different unrestricted conditions. Expression classification belongs to a pattern recognition problem where intra-class distance is greater than the inter-class distance, which leads to severe over-fitting when using neural networks for expression recognition. This paper proposes a novel network structure called Dimension Reduction Network which can effectively reduce generalization error. By adding a data dimension reduction module before the general classification network, a lot of redundant information is filtered, and only useful information is *** can reduce the interference by irrelevant information when performing classification tasks and reduce generalization error. The proposed method does not require any modification to the classification network, only a small dimension reduction module needs to be added in front of the classification network. However, it can effectively reduce generalization error. We designed big and tiny versions of Dimension Reduction Network, both exceeds our baseline on AffectNet data set. The big version of our proposed method surpassed the state-of-the-art methods by more than 1.2% on AffectNet data set. Our code will open source(3) when the paper is accepted.

关键词： Face recognition

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User-Biased Food Recognition for Health Monitoring 21st

User-Biased Food Recognition for Health Monitoring

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21st international conference on Image analysis and Processing (ICIAP)

作者： Hussain, Mazhar Ortis, Alessandro Polosa, Riccardo Battiato, Sebastiano Univ Catania Dept Math & Comp Sci Viale A Doria 6 I-95125 Catania Italy Univ Catania Ctr Excellence Accelerat HArm Reduct CoEHAR Catania Italy Univ Catania Dept Clin & Expt Med Catania Italy Spin Off Univ Catania ECLAT Srl Catania Italy

ISBN: (纸本)9783031064333;9783031064326

This paper presents a user-biased food recognition system. The presented approach has been developed in the context of the FoodRec project, which aims to define an automatic framework for the monitoring of people's health and habits, during their smoke quitting program. The goal of food recognition is to extract and infer semantic information from the food images to classify diverse foods present in the image. We propose a novel Deep Convolutional Neural Network able to recognize food items of specific users and monitor their habits. It consists of a food branch to learn visual representation for the input food items and a user branch to take into account the specific user's eating habits. Furthermore, we introduce a new FoodRec-50 dataset with 2000 images and 50 food categories collected by the iOS and Android smartphone applications, taken by 164 users during their smoking cessation therapy. The information inferred from the users' eating habits is then exploited to track and monitor the dietary habits of people involved in a smoke quitting protocol. Experimental results show that the proposed food recognition method outperforms the baseline model results on the FoodRec-50 dataset. We also performed an ablation study which demonstrated that the proposed architecture is able to tune the prediction based on the users' eating habits.

关键词： Dietary monitoring Food recognition Food dataset Artificial intelligence for health

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Modeling Viewpoint of Forklift Operators Using Context-Based Clustering of Gaze Fixations 23rd

Modeling Viewpoint of Forklift Operators Using Context-Based...

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23rd international conference on Human-Computer Interaction (HCII)

作者： Chew, Jouh Yeong Okuma, Takashi Yoshida, Eiichi Koide, Yukikazu AIST Natl Inst Adv Ind Sci & Technol Tsukuba Cent 1 1-1-1 Umezono Tsukuba Ibaraki 3058560 Japan

ISBN: (纸本)9783030909635;9783030909628

This study proposes a method to find optimal viewpoints for Human Machine Interface (HMI) of teleoperation system using human visual pattern. Forklift operation is used as a case study due to its operation complexity consisting of multiple work contexts such as driving and cargo handling. It is challenging to model human viewpoint because there is usually no prior knowledge of the behavior, complicated steps to process behavioral data, and difficulty to represent dynamic behavior throughout the operation. Therefore, a method is proposed to model human viewpoint using setups in a virtual environment that is reconstructed from a real laboratory environment mimicking the warehouse. Gaze points are measured during experiments in virtual environment, and the clustering methods are used to find natural spherical clusters resembling human foveal vision at different work contexts. Viewpoints of several category of forklift operators are derived from the proposed method, and their common viewpoints at each work context is represented using a piece-wise function defining distribution of cluster centroids from the origin of local coordinate system. Spatial analysis of gaze pattern suggests distribution of gaze centroids are generally spatially independent between work contexts. More importantly, the common viewpoint is spatially correlated with the viewpoints of different category of operators. This suggests the proposed model is representative of the general viewpoint of forklift operation.

关键词： Work context K-means clustering Hierarchical clustering Gaze fixations Human viewpoint Teleoperation Spatial analysis Clark-Evans criterion

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