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Enhancing handwritten text feature extraction through key point detection and graph representation

Soft Computing 2024年第21期28卷 12977-12990页

作者： Rahman, Atta Ur Alahmadi, Tahani Jaser Alsenani, Yousef S. Ali, Sania Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technologies P.O. Box 23460 Topi Pakistan P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science University of Science and Technology Bannu P.O. Box 28100 Khyber Pakhtunkhwa Bannu Pakistan

Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions;however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Identification and Monitoring Strategies for Multiphase Batch Processes based on Dissimilarity Analysis 43

Identification and Monitoring Strategies for Multiphase Batc...

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43rd Chinese Control Conference, CCC 2024

作者： Guo, Qingxiu Liu, Jianchang Tan, Shubin Wang, Honghai Zhang, Xinnan Zhang, Lijie Northeastern University The College of Information Science and Engineering Shenyang110819 China Northeastern University The National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ningxia Institute of Science and Technology The College of Electrical Information Engineering Ningxia753000 China

ISBN: (纸本)9789887581581

Batch processes often have multiphase characteristics, and the dynamic transition between phases cannot be ignored. Therefore, this paper proposes identification and monitoring strategies for multiphase batch processes based on dissimilarity analysis(DAIM). In the identification section, dissimilarity analysis is introduced, which can divide the transitions between different phases from the perspective of similarity. The average difference vector is proposed in the monitoring section. It could be used to evaluate the test sample deviation and provide new statistics to monitor the state of the test samples, which improves the fault detection rate. The method proposed in this paper was applied to the benchmark penicillin fermentation process, and the experimental results verified the effectiveness of the DAIM. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Batch data processing

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A Real- Time Multi-Camera Auto-Adjustment Framework for Infrastructure Inspections

A Real- Time Multi-Camera Auto-Adjustment Framework for Infr...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Phuoc Le, Chuong Ellison, Charles Bunkley, Steven La, Hung Netchaev, Anton Lab Department of Computer Science and Engineering RenoNV89557 United States Information Technology Lab VicksburgMS39180 United States

ISBN: (纸本)9798350312072

In this paper, we proposed a novel multi-camera auto-adjustment framework for infrastructure inspections, specifically 3D point cloud mapping via lidar-camera fusion. Our method uses a single controller for consensus target brightness and adaptive lighting. In addition, a vignette correction filter was created to apply to images outputted from cameras to avoid dark strips on the point cloud data. This approach is designed to be a versatile, real-time friendly, and platform agnostic software stack. © 2024 IEEE.

关键词： Cameras

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Clothed Human Model Estimation from Unseen Partial Point Clouds with Meta-learning 1st

Clothed Human Model Estimation from Unseen Partial Point Cl...

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Fang, Chenghao Wang, Kangkan Yin, Shihao Li, Shaoyuan Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819712793

This work aims to address the problems of clothed human reconstruction from unseen partial point clouds. Existing methods focus on estimating vertex offsets on top of parametric models for clothing details but with the limitation of the fixed topology, or reconstructing non-parametric shapes using implicit functions which are lack of semantic information. Moreover, due to limited training data, large variations in clothing details, and domain gaps between training data and real data, these methods often have poor generalization ability on real data. In this paper, we propose a generalizable approach for estimating dressed human models from single-frame partial point clouds based on meta-learning. Specifically, we first learn a meta-model that can efficiently estimate the parameters of unclothed human models from unseen data by a fast fine-tuning. Based on the unclothed human models, we further meta-learn the point-based human models in clothing with local geometric features, which is topologically flexible and rich in human details. Our approach outperforms previous work in terms of reconstruction accuracy, as evidenced by qualitative and quantitative results obtained from a variety of datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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Modeling and Solving of Platform Resource Scheduling Problem Based on Capability as a Service 7

Modeling and Solving of Platform Resource Scheduling Problem...

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7th International Conference on Computational Intelligence and Applications, ICCIA 2022

作者： Sun, Yu Mao, Shaojie Huang, Songhua Wang, Shuangling 1.Science and Technology on Information Systems Engineering Laboratory 2.Unit Nanjing94860 China Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781665495844

Command information system can realize flexible matching between platform resources and tasks on the basis of platform combat capability as a service. In order to satisfy the intelligent decision-making need of command information system, the platform resource scheduling problem under the condition of capability as a service is described mathematically. Then, a scheduling model aiming to maximize the task processing quality is constructed and a model solving algorithm with heuristic strategy is designed. Finally, the method is simulated and verified with detailed examples. The experiment results show that the method proposed is effective and efficient, which can support intelligent decision-making of command information system. © 2022 IEEE.

关键词： Scheduling

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In Situ Acoustic Emission Investigation of Asphalt Fracture Process and Damage Quantification for Different Asphalt Mixtures

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Journal of Nondestructive Evaluation, Diagnostics and Prognostics of engineering systems 2025年第3期8卷 031001页

作者： Zhu, Yu Wang, Haixu Sun, Xiaojun Li, Feng Guan, Xuefei Huang, Min He, Jingjing Beijing Research Institute of Aero Engines AECC Beijing101304 China Beijing Institute of Space Science and Technology Information Beijing100086 China School of Transportation Science and Engineering Beihang University Beijing100191 China Graduate School of China Academy of Engineering Physics Beijing100193 China School of Reliability and Systems Engineering Beihang University Beijing100191 China

The identification of cracking damage and the dynamic diagnosis of damage evolution are of great importance to prolong the service life of asphalt mixture materials. Acoustic emission (AE) can identify the formation and propagation of cracks by detecting the released energy of the damage;therefore, it provides a viable real-time technique to estimate the damage state in the context of structural health monitoring. In this study, the crack formation and propagation of three different asphalt mixtures were investigated using in situ AE monitoring and microscopy imaging. A three-point bending test was performed using specimens fabricated from three different types of asphalt mixtures. The variation of AE parameters such as the cumulative AE energy and the AE count was obtained and correlated with the crack sizes to characterize the damage process of the three mixtures. Based on the AE parameters, the whole damage process can be divided into three distinct phases, namely, the elastic deformation, damage accumulation, and crack propagation. The AE parameters in the three different mixtures show unique features, and they can be used to capture the transition between phases and identify the damage state. A universal power law model is proposed to correlate the AE energy and the crack density for different types of asphalt mixtures, providing a viable means for damage quantification and predictive maintenance. © 2024 by ASME.

关键词： Structural health monitoring

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract] 40

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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Automatic Detection of Some Tajweed Rules 20

Automatic Detection of Some Tajweed Rules

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20th International Learning and technology Conference, L and T 2023

作者： Omran, Dahlia Fawzi, Sahar Kandil, Ahmed Cairo University Systems and Biomedical Engineering Faculty of Engineering Giza Egypt Nile University Information Technology & Computer Science Giza Egypt

ISBN: (纸本)9798350300307

correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%. © 2023 IEEE.

关键词： Convolutional neural networks

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