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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

共 15923 条记录，以下是4871-4880 订阅

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CAB-Net: Channel Attention Block Network for Pathological Image Cell Nucleus Segmentation 11th

CAB-Net: Channel Attention Block Network for Pathological Im...

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11th international conference on Image and Graphics, ICIG 2021

作者： Li, Meixuan Fan, Huijie Yang, Dawei School of Information Technology and Engineering Shenyang Ligong University Liaoning Shenyang110159 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783030873578

In histopathological image analysis, cell nucleus segmentation plays an important role in the clinical analysis and diagnosis of cancer. However, due to the different morphology of cells, uneven staining and the existence of a large number of dense nuclei, it is still challenging to accurately segment the nucleus. In order to learn more specific key feature information during the training process, this paper proposed a network model called the CAB-Net that uses channel attention to enhance the learning of feature information on each channel. The network uses the channel attention mechanism to extract the key features in each channel, generates weights to judge the importance of the features, and then weights them into the original image. This aims to strengthen the extraction of key information, thereby generating a more characteristic feature map, and enabling the model to make more accurate judgments. We added a boundary smoothness constraint, in order to better identify cell nuclei with unclear boundaries and achieve more accurate cell nucleus segmentation. The experimental results show that the method in this paper achieves good performance on the cell segmentation data set. © 2021, Springer Nature Switzerland AG.

关键词： Cells

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Adaptive Scaling for Archival Table Structure Recognition 16th

Adaptive Scaling for Archival Table Structure Recognition

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16th IAPR international conference on Document analysis and Recognition (ICDAR)

作者： Li, Xiao-Hui Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9783030865498

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.

关键词： Archival document Table detection Table structure recognition Adaptive scaling

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

32nd International Conference on Database and Expert Systems...

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030864712

The proceedings contain 68 papers. The special focus in this conference is on Database and Expert Systems Applications. The topics include: Sarcasm Detection for Japanese Text Using BERT and Emoji;Sigmalaw PBSA - A Deep Learning Model for Aspect-Based Sentiment analysis for the Legal Domain;BERT-Based Sentiment analysis: A Software Engineering Perspective;a Stochastic Block Model Based Approach to Detect Outliers in Networks;Medical-Based Text Classification Using FastText Features and CNN-LSTM Model;diversified pattern Mining on Large Graphs;EHUCM: An Efficient Algorithm for Mining High Utility Co-location patterns from Spatial Datasets with Feature-specific Utilities;BERT-Based Multi-Task Learning for Aspect-Based Opinion Mining;GPU-Accelerated Vertex Orbit Counting for 5-Vertex Subgraphs;subgroup Discovery with Consecutive Erosion on Discontinuous Intervals;efficient Discovery of Partial Periodic-Frequent patterns in Temporal Databases;database Framework for Supporting Retention Policies;internal Data Imputation in Data Warehouse Dimensions;purging Data from Backups by Encryption;improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing;Online Optimized Product Quantization for Dynamic Database Using SVD-Updating;querying Collections of Tree-Structured Records in the Presence of Within-Record Referential Constraints;Dealing with Plethoric Answers of SPARQL Queries;feature Selection and Software Defect Prediction by Different Ensemble Classifiers;traffic Flow Prediction Through the Fusion of Spatial-Temporal Data and Points of Interest;Fast SQL/Row pattern Recognition Query Processing Using Parallel Primitives on GPUs;predicting Student Performance in Experiential Education;log-Based Anomaly Detection with Multi-Head Scaled Dot-Product Attention Mechanism;enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and analysis in Emerging Environments.

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A Novel Method for Detecting the Width of Tobacco Filaments Based on Coordinate Point Fitting 23

A Novel Method for Detecting the Width of Tobacco Filaments ...

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Proceedings of the 2023 international Joint conference on robotics and Artificial intelligence

作者： Jinxin Tie Yuefeng Wang Weizheng Zhang Meng Li Daojian Yang Jinhua Song Maosong Li Junhui Wang Mingfang Zheng Chunbo Yuan Yue Long Yujie Wu Jie Li Ningbo Cigarette Factory China Tobacco Zhejiang Industrial Co. Ltd. China School of Computer and Communication Engineering Zhengzhou University of Light Industry China College of tobacco science and engineering Zhengzhou University of Light Industry China

ISBN: (纸本)9798400707704

Abstract. The width of the tobacco filament (WTF) is a significant process parameter in cigarette production as it has a crucial impact on both the physical quality and sensory experience of the product. The detection methods currently employed for WTF mainly involve manual visual inspection or low-frequency offline inspection, which lack representation, accuracy, and efficiency. To enhance the detection efficiency and accuracy, a method for detecting WTF images utilizing the coordinate point fitting method (CPFM) is proposed. We compare the performance of CPFM with the conventional Hough transform method (HTM) and the circle with different radii method (CDRM). Based on experimental results, CPFM has a relative error of 5.3%, an absolute error of 0.085 mm, and a maximum relative standard deviation of 1% in six repetitive experiments. This suggests that the suggested CPFM technique is dependable and dependable and fulfills the statistical demands of metrology. By implementing the WTF image detection method, which relies on the coordinate point fitting methodology, detection efficiency and accuracy are substantially increased. The technique necessitates rapid and exact WTF detection and surpasses prior approaches significantly. Compared to traditional methods, the CPFM method exhibits distinct benefits in detecting WTF. It is probable that the method will become widely used in cigarette production to improve both product quality and production efficiency.

关键词： Coordinate point fitting Detection efficiency Morphological analysis Tobacco filaments Width detection

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Counter-Misdirection in Behavior-based Multi-robot Teams **Research supported by the National Science Foundation under CNS EAGER grant #1848653.

Counter-Misdirection in Behavior-based Multi-robot Teams **R...

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IEEE international conference on intelligence and Safety for robotics (ISR)

作者： Shengkang Chen Ronald C. Arkin Electrical and Computer Engineering Interactive Computing Georgia Institute of Technology Atlanta GA USA

When teams of mobile robots are tasked with different goals in a competitive environment, misdirection and counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the number of approaches on counter-misdirection for multi-robot systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by deploying a new type of agent: counter-misdirection agents (CMAs). These agents can detect the misdirection process and “push back” the misdirected agents collaboratively to stop the misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical robotic testbed to study its effectiveness. It shows that this approach can stop the misdirection process effectively with a sufficient number of CMAs. This novel counter-misdirection approach can potentially be applied to different competitive scenarios such as military and sports applications.

关键词： conferences Safety Multi-robot systems Mobile robots Task analysis Sports

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Fight Detection in Crowd Scenes Based on Deep Spatiotemporal Features 2

Fight Detection in Crowd Scenes Based on Deep Spatiotemporal...

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3rd international conference on Artificial intelligence, robotics and Control (AIRC)

作者： Almazroey, Alaa Atallah Jarraya, Salma Kammoun King Abdulaziz Univ Fac Comp & Informat Technol Comp Sci Dept Jeddah Saudi Arabia Univ Sfax MIR CL Lab Sfax Sfax Tunisia

ISBN: (纸本)9781450389266

Over the last few decades, remarkable growths of distributing surveillance cameras have been noticed in privet and public facilities. Therefore, with a growing demand for security and to ensure public safety. An intelligent automated approach is essential for on-spot violence and fight detection as it would save the time and cost of manual fight detection from monitor screens. In this research, we propose a supervised deep learning-based approach to detect fight actions from crowd video scenes. By extracting the keyframes from video frames using the cosine similarity algorithm, using them to compute optical flow values of the magnitudes, orientation and velocity. Afterward, using these values to construct four 2D templates, which are supplied to the pre-trained network to extract deep spatiotemporal features. Finally, applying the Neighborhood Component analysis (NCA) feature selection method, and the Vector Support Machine (SVM) classifier, to generate a model that able to identify fighting behavior from crowd video scenes. Three different public datasets, the Hockey dataset, Movies dataset and Violent-Flows dataset, were used to evaluate the proposed method. The efficiency of the proposed method outperformed other state-of-the-art methods in terms of accuracy and the required time for fight detection to be executed.

关键词： Deep Learning Fight Detection Keyframe Selection Neighborhood Component analysis Surveillance Videos

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analysis of Traditional Italian Food Recipes: Experiments and Results 25th

Analysis of Traditional Italian Food Recipes: Experiments an...

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25th international conference on pattern Recognition Workshops, ICPR 2020

作者： Artese, Maria Teresa Ciocca, Gianluigi Gagliardi, Isabella Milan Italy Universitá degli Studi di Milano-Bicocca Milan20126 Italy

ISBN: (纸本)9783030688202

Traditional recipes are among those elements that UNESCO included in its Intangible Cultural Heritage for safeguarding. Traditional recipes are passed down from one generation to the other, and offer strong links with a particular territory. Driven by the important role of food recipes in the cultural heritage domain, we have create CookIt, a web portal with the aim to collect, disseminate and safeguard the knowledge of typical Italian recipes and the Mediterranean diet which is a significant part of the Italian cuisine. In this paper we present some preliminary results in recipe analysis to be used within our web portal to support innovative ways to navigate and browse them. We developed some processing and visualization tools to support the analysis and the presentation of the recipes. Our tools are tailored for the Italian language although they can be generalized. © 2021, Springer Nature Switzerland AG.

关键词： Portals

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Link Prediction in Social Networks by Variational Graph Autoencoder and Similarity-Based Methods: A Brief Comparative analysis 25th

Link Prediction in Social Networks by Variational Graph Auto...

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25th international conference on pattern Recognition Workshops, ICPR 2020

作者： Roy, Sanjiban Sekhar Ranjan, Aditya Tomasiello, Stefania School of Computer Science and Engineering Vellore Institute of Technology Vellore India Institute of Computer Science University of Tartu Tartu Estonia

ISBN: (纸本)9783030687984

Link prediction is an emerging and fast-growing applied research area. In a network, it is possible to predict the next link which is going to be formed. The usefulness of link prediction modeling has been proved in several fields and applications, such as biomedicine, recommending systems, and social media. In this short paper, we discuss the potential of Variational Graph Autoencoder, by comparing the results so obtained against those by some similarity-based methods, such as Adamic-Adar, Jaccard coefficient, and Preferential Attachment. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

32nd International Conference on Database and Expert Systems...

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32nd international conference on Database and Expert Systems Applications, DEXA 2021

ISBN: (纸本)9783030864743

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Document Dewarping with Control Points 16th

Document Dewarping with Control Points

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16th IAPR international conference on Document analysis and Recognition (ICDAR)

作者： Xie, Guo-Wang Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat 95 Zhongguancun East Rd Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9783030865498

Document images are now widely captured by handheld devices such as mobile phones. The OCR performance on these images are largely affected due to geometric distortion of the document paper, diverse camera positions and complex backgrounds. In this paper, we propose a simple yet effective approach to rectify distorted document image by estimating control points and reference points. After that, we use interpolation method between control points and reference points to convert sparse mappings to backward mapping, and remap the original distorted document image to the rectified image. Furthermore, control points are controllable to facilitate interaction or subsequent adjustment. We can flexibly select post-processing methods and the number of vertices according to different application scenarios. Experiments show that our approach can rectify document images with various distortion types, and yield state-of-the-art performance on real-world dataset. This paper also provides a training dataset based on control points for document dewarping. Both the code and the dataset are released at https://***/gwxie/Document-Dewarping-with-Control-Points.

关键词： Dewarping document image Control points Deep learning

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