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检索条件"任意字段=27th Conference on Intelligent Robots and Computer Vision: Algorithms and Techniques"

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification 6

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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6th IEEE International conference on Power, intelligent Computing and Systems, ICPICS 2024

作者： Zhong, Herun School of Arts and Design Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9798350374315

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. this article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. the discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks. © 2024 IEEE.

关键词： Convolutional neural networks

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ICACS 2024 - Proceedings of the 2024 8th International conference on algorithms, Computing and Systems

ICACS 2024 - Proceedings of the 2024 8th International Confe...

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8th International conference on algorithms, Computing and Systems, ICACS 2024

ISBN: (纸本)9798400718304

the proceedings contain 24 papers. the topics discussed include: autonomous floating garbage collection device using computer vision and robotics;coordinated optimization of scheduling and path planning for delivery robots in intelligent manufacturing workshops;a preliminary study of the stressed and drowsy driving prediction models using semi-supervised learning;axial capacity prediction of concrete externally strengthened by low-cost natural fiber reinforced polymer composite utilizing machine learning intelligence;enhancing brain tumor diagnosis: a cutting-edge ensemble deep learning approach;a comparative study of sentiment analysis on twitter and reddit using deep learning techniques;generative artificial intelligence for future education: current research status, hot spots, and research trends;methodological considerations for anonymizing tabular data using generative adversarial networks;and data migration in large scale storage systems with varying file sizes.

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Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional computer vision

Exploring the Trade-Off Between Performance and Cost in Faci...

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intelligent Systems conference (IntelliSys)

作者： Chen, Jan-Jo Husnain, Ali Cheng, Wei-Wei Chicago State Univ Chicago IL 60628 USA Northrop Grumman Chicago IL 60606 USA

ISBN: (纸本)9783031477232;9783031477249

During the late 20th century, the rise of computers and technology led to collaborations between mathematicians and computer scientists to develop systems that could solve various problems using traditional approaches. One such problem was facial recognition, which enables the identification of human faces without interference. Traditional machine learning methods were used to build these systems until recently, when deep neural networks trained with massive datasets replaced them. Deep learning has pushed the boundaries of what is achievable in facial recognition. However, traditional computer vision methods, which had been improving for years prior to the emergence of deep learning, still have room for improvement. this study aims to compare the accuracy of facial recognition algorithms, specifically FaceNet and Eigenfaces, from deep learning and traditional computer vision domains. the availability of physical machine power and dataset scale creates a tradeoff between performance and cost. Traditional computer vision techniques may be suitable for low-power applications, while deep neural networks provide better accuracy results when high-powered machines are available. the performance benefits of deep learning outweigh the data scale and training execution time required.

关键词： Deep learning Face recognition Eigenfaces FaceNet

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate 27

Short-term Portfolio Optimization using Doubly Regularized E...

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27th International conference on computer Supported Cooperative Work in Design (CSCWD)

作者： Guan, Quanlong He, Jinneng Lai, Zhao-Rong Hou, Yuyu Jiang, Quming Chen, Ziliang Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Guangdong Inst Smart Educ Guangzhou Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou Peoples R China Chutian Dragon Co Ltd Dongguan Peoples R China Guangdong Macao Adv Intelligent Comp Joint Lab Zhuhai Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. this paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. the approach uniquely combines l(1) and l(2)-regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. this algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Short-term portfolio optimization machine learning in finance application FinTech exponential growth rate approach

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computer vision-Based Method for Digital Twin Modelling in Railway 27

Computer Vision-Based Method for Digital Twin Modelling in R...

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27th IEEE International conference on intelligent Transportation Systems, ITSC 2024

作者： Zhang, Shirui Leromancer, Briac Nicodeme, Claire and Sncf France Sncf Sa Research and Technology Department Saint Denis France Mines Paris-PSL University and Sncf France

ISBN: (纸本)9798331505929

Efficient maintenance requires knowledge and expertise about the equipment. this is especially true for public transportation and railway, where security is one of the most important pillars. However, trains and related systems are complex, making expertise difficult to acquire. In addition, given the long lifecycle of railway equipment and infrastructures, some may no longer be documented. Yet, retrieving essential information with the required details is needed. To provide the necessary data, the authors present a novel digital twin-based concept for advanced real-world information gathering. Different modelling techniques such as LiDAR Scanning, RGB-D Scanning and Deep Learning-based scene reconstruction are investigated to create texture-aware 3D models of different railway objects. Representation quality, scale adaptation and access to technologies are discussed. From this perspective, these models are promising for object recognition, predictive maintenance, extended reality (XR) tools development and agents training in intelligent Transportation Systems (ITS). © 2024 IEEE.

关键词： Predictive maintenance

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Reexamining Indoor Mobile robots from a Cognitive Perspective 5

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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5th International conference on Robotics and computer vision, ICRCV 2023

作者： Song, Fei Li, Jinyu Tang, Fengzhen Tang, Yandong Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

ISBN: (纸本)9798350326369

the field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. this article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. these phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques. © 2023 IEEE.

关键词： Navigation

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Industrial 5.0 Aquaponics System Using Machine Learning techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

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7th International conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. the primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. the industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. this capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 IEEE.

关键词： Decision making

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An U-Net Semantic Segmentation vision System on a Low-Power Embedded Microcontroller Platform 27

An U-Net Semantic Segmentation Vision System on a Low-Power ...

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27th International conference on Knowledge Based and intelligent Information and Engineering Sytems, KES 2023

作者： Falaschetti, Laura Bruschi, Sara Alessandrini, Michele Biagetti, Giorgio Crippa, Paolo Turchetti, Claudio DII - Department of Information Engineering Università Politecnica delle Marche via Brecce Bianche 12 AnconaI-60131 Italy Università Politecnica delle Marche via Brecce Bianche 12 AnconaI-60131 Italy

In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. the rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a UNet as base architecture. the goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. the lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Embedded Systems Microcontrollers Semantic Segmentation U-Net Visual SLAM

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HS-Gen: Human-Like Handwriting Synthetic Generation—A Preliminary Investigation 27th

HS-Gen: Human-Like Handwriting Synthetic Generation—A Preli...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Bensalah, Asma Parziale, Antonio Coccaro, Rosanna Marcelli, Angelo Fornés, Alicia Lladós, Josep Computer Vision Center Universitat Autònoma de Barcelona Barcelona Spain Computer Science Department Universitat Autònoma de Barcelona Barcelona Spain DIEM University of Salerno Via Giovanni Paolo II 132 SA Fisciano84084 Italy AI3S Unit CINI National Laboratory of Artificial Intelligence and Intelligent Systems University of Salerno SA Fisciano Italy

ISBN: (纸本)9783031876592

the application of handwriting analysis in the health field for early detection and diagnosis is limited by a lack of data, which presents a significant challenge for the implementation of deep learning-based models. To address this issue, numerous studies have focused on generating synthetic data. Current methods for generating synthetic handwriting from offline images, including generative models and geometrical techniques, fail to account for the kinematics of handwriting movements, which is critical for achieving more human-like results. To address this limitation, we propose a novel, human-like approach for the synthetic generation of handwriting or drawing from offline images. this method creates new samples by incorporating a trajectory recovery algorithm and a human-like time law generator, as well as the extraction and manipulation of kinematic parameters. the evaluation of the proposed method from both visual and kinematic perspectives demonstrates its potential applicability across a wide range of devices and handwriting styles. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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An intelligent Pipeline for Localization of Industrial Components in Robotic Manufacturing Applications 27

An Intelligent Pipeline for Localization of Industrial Compo...

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27th European conference on Artificial Intelligence, ECAI 2024

作者： Rawal, Parth Valencia, Daniel Hintze, Wolfgang Ottenbecker Damm 12 Stade21684 Germany Hamburg University of Technology TUHH Denickestraße 15 Hamburg21071 Germany

ISBN: (纸本)9781643685489

the rising skill shortage problem in Europe threatens the economic slowdown in the manufacturing sector. Approaches based on artificial intelligence can play a crucial role in bridging the shortage gap if they can be integrated into robot-assisted production to simplify repetitive manual tasks. Localizing components in a production cell is a familiar problem of robot-assisted production. robots are often taught trajectories manually, which requires expertise in robot programming. Some of the existing feature-based computer vision solutions can localize a component in 3D space. However, these solutions are not versatile enough to be integrated across different components and production cells. this paper proposes an AI-based solution in the form of a pipeline for the 6D localization of components that can be integrated into multiple industrial use cases. the pipeline encompasses flows for generating synthetic images of components from their CAD model, training deep neural networks to estimate component poses, and improving their accuracy for manufacturing applications. the performance of the pipeline has been validated for components in a production-related environment. the paper also demonstrates the versatility of the pipeline by deploying it for a robotic spray coating use case. Such AI skills can empower the skilled workforce on the shop floor so that they can focus on the overall manufacturing process. © 2024 the Authors.

关键词： Deep neural networks

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