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Research on Satellite Navigation and Positioning Based on Laser Point Cloud Data 14th

Research on Satellite Navigation and Positioning Based on La...

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14th International symposium on Intelligence Computation and Applications, ISICA 2023

作者： Sun, Yuming Wang, Hua Guangdong University of Science and Technology Dongguan523000 China

ISBN: (纸本)9789819743957

the existing satellite navigation and positioning technology can’t meet the requirement of target positioning accuracy, and the results are not obtained in time. In order to improve the accuracy and real-time of satellite navigation and positioning, a satellite navigation and positioning method based on laser point cloud data is proposed. In order to reduce the influence of interference factors, the mean filter method is used to denoise the laser point cloud data, and the Euclidean threshold region segmentation method is used to segment discrete data points. Aiming at extracting geometric features of objects, corner features of point cloud data are extracted based on geometric features. On the basis of the preprocessing results, the absolute position information, angular velocity and driving speed of satellite navigation are acquired by laser point cloud data, and the navigation datum line and characteristic points in the navigation path are acquired by image processing in machine vision. Satellite navigation and positioning based on laser point cloud data are completed. the simulation results show that the proposed method can effectively improve the positioning accuracy and operation efficiency, and can meet the practical application requirements. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image processing

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Contrastive Learning and Iterative Meta-Pseudo-Labeling on 2D Projections for Deep Semi-Supervised Learning

Contrastive Learning and Iterative Meta-Pseudo-Labeling on 2...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： David Aparco-Cardenas Jancarlo F. Gomes Alexandre X. Falcão Pedro J. De Rezende Institute of Computing (IC) University of Campinas (UNICAMP) Campinas Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

the scarcity of accurately labeled data critically hampers the usage of deep learning models. this issue is highlighted in areas (e.g., biological sciences) where data annotation results in an expert-demanding, labor-intensive and error-prone task. While state-of-the-art semi-supervised approaches have proven effective in circumventing this limitation, their reliance on pre-trained architectures and large validation sets to deliver effective solutions still poses a challenge. In this work we introduce an iterative contrastive-based meta-pseudo-Iabeling method for training non-pre-trained custom CNN architectures for image classification in conditions of limited labeled and abundant unlabeled data, with no dependency on a validation set. It generates multiple models across a few iterations, which are in turn exploited in an ensemble manner to label the unlabeled data and train a final classifier. Our approach starts by capitalizing on contrastive learning to enhance the representation ability of two collaborative networks while eliminating the need of pre-trained architectures. then, during each iteration, the networks are trained within a teacher-student based cross-training setup, where OPFSemi (teacher) propagates labels from labeled to unlabeled on the non-linear 2D latent space projections of each network's (student) deep features; afterward, the pseudo-labels with the highest top 10% confidence, per class, are picked to fine-tune the other network in a cross-training manner, jointly mitigating confirmation bias and overfitting while improving the generalization ability of the networks as iterations evolve. Our method is evaluated on three challenging biological image datasets with only 5 % of labeled samples, demonstrating its effectiveness and robustness when compared to two direct baselines and six state-of-the-art methods from three different semi-supervised learning paradigms.

关键词： Training Federated learning Annotations Contrastive learning Semisupervised learning Robustness Data models Iterative methods Labeling image classification

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Automatic Football Match Event Detection from the Scoreboard using a Single-Shot MultiBox Detector

Automatic Football Match Event Detection from the Scoreboard...

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International Joint symposium on Artificial Intelligence and Natural Language processing (iSAI-NLP)

作者： Rungroj Somwong Narit Hnoohom Department of Computer Engineering Image Mahidol University Nakorn Pathom Thailand

ISBN: (数字)9781728156316

ISBN: (纸本)9781728156323

During a football match, the information is manually collected by humans. However, the correctness of the football match data is difficult to check because of the game's speed, and thus, human errors can occur. this paper presents an automatic football match event detection from the scoreboard using a deep learning algorithm. the proposed method can reduce human error and performs the detection faster. In this study, the detection was trained with 30,000 data of Goals, Substitutions and Cards scoreboard from 68 matches of English Premier League 2017-2018 broadcast videos. the detection was tested with 80 sub-testing videos. these videos were prepared from 20 full matches broadcast videos, which consisted of 12 full matches from the year 2017-2018 and 8 full matches from the year 2018-2019. the proposed method contains three main steps: data gathering and augmentation, object detection for scoreboard visualization forms, and the event classification. the scoreboard detection is performed with a Single-Shot MultiBox Detector. the event classification employs the majority vote and time frame technique. the experimental results show an accuracy rate of 1.00 with the expected event scoreboards, comprised of Goal, Substitution, and Card events.

关键词： Videos Sports Event detection Object detection Annotations Training

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Advanced Virtual Human Modeling with Metahumans: Focus on Genderless Characters

Advanced Virtual Human Modeling with Metahumans: Focus on Ge...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Victor Flávio de Andrade Araujo Ana Schmitt Angelo B. Costa Natália Dal Pizzol Soraia R. Musse Virtual Human Lab School of Technology PUCRS Porto Alegre Brazil National Institute of Science and Technology Social and Affective Neuroscience INCT-SANI Prejudice Vulnerability and Psychosocial Processes Group School of Health and Life Sciences PUCRS Porto Alegre Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Virtual Humans (VHs) with high levels of anthropomorphism in visual appearance and behavior can enhance user experience in movies, games, and other interactive media. Users often seek human-like representations that include realistic movement, emotion, and gender, fostering a stronger sense of identification with VHs. Studies from Psychology have shown that people tend to evaluate characteristics of others within their own group differently, known as the in-group advantage. For example, women may be better at recognizing emotions in other women than in men. Researchers have also noted differences in feature recognition based on both the gender of the person and the gender of the perceiver, a phenomenon that also extends to VHs. Understanding how humans perceive VHs is crucial for improving user experience and representation in virtual environments. Gender is a key anthropomorphic characteristic in VHs, essential for representativeness, human identification, and user comfort. Typically, VHs assigned a specific gender exhibit stereotyped features such as movements, clothing, hairstyles, and colors, designed to be easily recognized by users. Insights into gender representation in VHs can guide the industry in modeling and animating VHs to achieve the desired impact. this tutorial introduces a methodology for modeling gender in VHs, from genderless virtual babies to genderless virtual adults, focusing on visual and behavioral aspects. Participants will learn how to create genderless VHs, enhancing gender representation and making virtual environments more inclusive for a diverse audience.

关键词： Industries Visualization Pediatrics image color analysis Virtual environments Psychology Tutorials Media Motion pictures User experience

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Approximate Multiply-Accumulate Array for Convolutional Neural Networks on FPGA

Approximate Multiply-Accumulate Array for Convolutional Neur...

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International Workshop on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC)

作者： Ziwei Wang Martin A. Trefzer Simon J. Bale Andy M. Tyrrell University of York York United Kingdom

ISBN: (数字)9781728147703

ISBN: (纸本)9781728147710

Convolutional Neural Networks (CNNs) have been widely used in many computer applications. the growth in deep neural networks and machine learning applications has resulted in the state-of-the-art in CNN architectures becoming more and more complex. Millions of multiply-accumulate (MACC) operations are needed in this kind of processing. To deal with these massive computing requirements, accelerating CNNs on FPGAs has become a viable solution for balancing power efficiency and processing speed. In this paper, we propose an approximate high-speed implementation of the convolution stage of a CNN computing architecture, the Approximate Multiply-Accumulate Array. Compared with the traditional multiply-accumulate operation, this implementation converts multiplications into additions and systolic accumulate operations. A key feature is the logarithmic addition with iterative residual error reduction stages which, in principle, allows to trade off power, area and speed with accuracy through for specific data using different configurations. Here, we present experiments where we configure the approximate multiplier in different ways, changing number of iteration stages as well as the bit width of the data and investigate the impact on overall accuracy. In this paper we present initial experiments evaluating the architecture's error using random input data, and Sobel Edge detection is used to investigate the proposed architecture with regard to its use in image-processing CNNs. the experimental results show that the proposed approximate architecture is up to 10.7% faster than a competitive FPGA implementation of an exact multiplier when running the convolution kernel over a test image, and that residual errors after two iterations reach 1.6% for 8-bit inputs and 0.001% for 12-bit inputs on average, based on 10,000 random samples.

关键词： Field programmable gate arrays computer architecture Kernel Power demand Logic gates Hardware Biological neural networks

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Goods Anomaly Detection Based on Multi-Branch Feature Extraction and Foreground Mining

Goods Anomaly Detection Based on Multi-Branch Feature Extrac...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction Information technology Anomaly detection Biomedical imaging

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A Teaching Effect Feedback Platform Combined with Convolutional Neural Networks

A Teaching Effect Feedback Platform Combined with Convolutio...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Wentong Li Jiwei Wang Taizheng Wang Zhaohui Yang Guobo Xing Xinfeng Liu College of Computer Science and Technology Shandong Jianzhu University Jinan China College of Civil Engineering Shandong Jianzhu University Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

At present, the rapid development of information technology has promoted the diversification of education methods. Online education, as an important supplement to traditional education and an important means of education reform, has become an integral part of the education and teaching process. However, the long-term development of online education still faces many problems, such as difficulties in grasping students' classroom learning states, poor interaction between teachers and students, and difficulty in ensuring the teaching effect. thanks to the development of deep learning technology and the availability of large-scale facial expression databases, significant progress has been made in the field of facial expression recognition. In this paper, we propose a teaching platform combined with artificial intelligence. this platform uses CNN (Convolutional Neural Network) to recognize students' classroom behavior, specifically their facial expressions, to analyze their learning states and provide timely feedback on classroom teaching effects. the platform can also read video, use advanced image processing and pattern recognition to find expressions and gestures in the video, and mark them, thereby recognizing students' classroom statuses and monitoring their listening statuses. this platform can also generate student behavior reports and visual statistical charts to help teachers better understand students' learning situations and progress, provide teachers with accurate and real-time teaching feedback, help teachers adjust teaching strategies and methods, and improve teaching quality. Finally, after the preliminary test, it is confirmed that the platform has met the initial design requirements and has high identification accuracy and strong data analysis abilities.

关键词： Uniform resource locators Visualization Accuracy Face recognition Education Big Data Streaming media Real-time systems Convolutional neural networks Information technology

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