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检索条件"任意字段=Conference on Optomechatronic Computer-Vision Systems II"

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

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IEEE/CVF conference on computer vision and Pattern Recognition (CVPR)

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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Converging human and algorithmic biases in the hiring decision-making process 6

Converging human and algorithmic biases in the hiring decisi...

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6th International conference on Intelligent systems and computer vision, ISCV 2024

作者： Aminou, Loubna Daaif, Abdelaziz Soulami, Maha Chalfaouat, Abderrahim Youssfi, Mohamed Hassan II University of Casablanca IIACS Laboratory ENSET of Mohammedia Mohammedia Morocco Hassan II University of Casablanca Moroccan-American Studies LAB Mohammedia Morocco

ISBN: (纸本)9798350350180

The intersection of human expertise and cutting-edge Artificial Intelligence (AI) marks the beginning of a new era in intelligent and efficient hiring systems. However, each also introduces specific prejudices from both humans and algorithms, leading to systemic flaws and inaccuracies. This narrative review explores the intersection between human and algorithmic biases within AI-empowered hiring systems, highlighting their impacts and the urgent need for solutions. The aim of this research is to build a foundational knowledge base by examining how these biases manifest and their consequences, that could drive the field towards a more equitable and efficient hiring process, free from biases and discrimination. © 2024 IEEE.

关键词： Intelligent systems

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Unveiling Graph Power: SegmentAnything and GCN Synergy for Instance Segmentation and Classification

Unveiling Graph Power: SegmentAnything and GCN Synergy for I...

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IEEE conference on Evolving and Adaptive Intelligent systems (IEEE EAIS)

作者： Scarrica, Vincenzo Mariano Staiano, Antonino Univ Naples Federico II Natl PhD Program AI Agrifood & Environm Corso Umberto I 40 I-80138 Naples Italy Univ Naples Parthenope Dept Sci & Technol Ctr Direz Isola C4 I-80143 Naples Italy

ISBN: (纸本)9798350366235;9798350366242

In this paper, we propose a novel approach to improve instance segmentation and classification tasks by incorporating SegmentAnything [1] before using graph neural network frameworks. Before experimentation, we ensure comprehensive data labeling using SegmentAnything for instance segmentation and Grounding DINO [2] for class annotation. Our study aims to evaluate the effectiveness of this integration by conducting a comparative analysis with the state-of-the-art vision Transformer model [3]. Surprisingly, our experiments reveal that while the vision transformer has demonstrated remarkable performance in various tasks, it underperforms compared to our proposed approach on the same dataset. Our findings underscore the efficacy of integrating SegmentAnything with graph neural networks for instance segmentation and classification tasks, emphasizing the pivotal of those networks in advancing computer vision methodologies.

关键词： instance segmentation object classification graph neural networks transformers

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visionKG: Unleashing the Power of Visual Datasets via Knowledge Graph 21st

VisionKG: Unleashing the Power of Visual Datasets via Knowle...

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21st International conference on The Semantic Web (ESWC)

作者： Yuan, Jicheng Anh Le-Tuan Manh Nguyen-Duc Tran, Trung-Kien Hauswirth, Manfred Danh Le-Phuoc Tech Univ Berlin Open Distributed Syst Berlin Germany Bosch Ctr Artificial Intelligence Renningen Germany Fraunhofer Inst Open Commun Syst Berlin Germany

ISBN: (纸本)9783031606342;9783031606359

The availability of vast amounts of visual data with diverse and fruitful features is a key factor for developing, verifying, and benchmarking advanced computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited data distribution for very specific fields of interest, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the vision Knowledge Graph (visionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-theart visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that visionKG is not only based on metadata but also utilizes a unified data schema and external knowledge bases to integrate, interlink, and align visual datasets. It enhances the enrichment of the semantic descriptions and interpretation at both image and instance levels and offers data retrieval and exploratory services via SPARQL and natural language empowered by Large Language Models (LLMs). visionKG currently contains 617 million RDF triples that describe approximately 61 million entities, which can be accessed at https://*** and through APIs. With the integration of 37 datasets and four popular computer vision tasks, we demonstrate its usefulness across various scenarios when working with computer vision pipelines.

关键词： computer vision Knowledge Graph Linked Data Ontology RDF

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IMAGE SEGMENTATION APPROACHES: A COMPARATIVE STUDY 9

IMAGE SEGMENTATION APPROACHES: A COMPARATIVE STUDY

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9th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International conference on Connected Smart Cities, CSC 2024 and the 16th International conference on e-Health, EH 2024, Part of the 18th Multi conference on computer Science and Information systems 2024, MCCSIS 2024

作者： Miftah, Rajaa Hanoune, Mostafa Bentaib, Mohssine LTIM Laboratory Faculty of Science Ben M’sik Hassan II University of Casablanca Casablanca20000 Morocco

ISBN: (纸本)9789898704597

Object detection is an intriguing and pivotal topic in the field of computer vision. Meanwhile, segmentation plays a crucial role and stands as one of the oldest problem statements that researchers have extensively investigated in the realm of Artificial intelligence (AI). Within computer vision systems, image segmentation poses significant challenges. Consequently, numerous methods have been developed to address this complex task. Given the vast array of options available, this research focuses on exploring and comparing the frequently employed classical techniques for image segmentation. The study aims to shed light on the strengths, limitations, and performance characteristics of each approach. By comprehensively examining these traditional segmentation methods, we strive to provide valuable insights into their applicability, potential use cases, and overall effectiveness in addressing the challenges posed by image segmentation tasks. Ultimately, this research seeks to contribute to the advancement of computer vision systems and inspire further exploration in the field of image segmentation. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Artificial Intelligence computer vision Image Segmentation Object Detection Scene Understanding Segmentation Techniques

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M2M Interface for IoT Traffic Light with computer vision and AnyLogic PLE 16th

M2M Interface for IoT Traffic Light with Computer Vision and...

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16th International conference on Computational Collective Intelligence (ICCCI)

作者： Mansurova, Madina Belgibayev, Baurzhan Abdrakhim, Sanzhar Boltaboyeva, Assiya Baigarayeva, Zhanel Sarsembayeva, Talshyn Al Farabi Kazakh Natl Univ Alma Ata Kazakhstan Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9783031702587;9783031702594

This paper proposes an advanced traffic light control system using IoT devices and computer vision, integrated through M2M interactions and modeled with AnyLogic PLE. The key contribution is the combination of IoT and computer vision for real-time, adaptive traffic light control. The study highlights the practical value of M2M technology, facilitating seamless interaction between web camera-equipped traffic lights and personal computers, overcoming the complexity of traditional wired methods like Siemens microcontrollers. Using a socket library for communication between Windows and Linux-based Raspberry Pi, the system implements interactive Wi-Fi information exchange for video monitoring and real-time road situation recognition. These data inputs control traffic lights via computer vision, enabling automated, adaptive traffic management. The prototype demonstrates real-time animated simulation managed by a dispatcher, enhancing the efficiency of traffic systems. The integration of M2M, IoT, and computer vision marks a significant advancement in intelligent transportation systems.

关键词： M2M Interaction Client-Server Interaction IoT Traffic Light Raspberry Pi 4 YOLOv8n Neural Network AnyLogic PLE

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Vehicle detection, classification and tracking in urban environments using computer vision

Vehicle detection, classification and tracking in urban envi...

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2024 Smart systems Integration conference and Exhibition

作者： Velasco, Jorge Ingelmo, Marta Romo, Javier Cidaut Fdn Connected & Sustainable Mobil Valladolid 47151 Spain

ISBN: (纸本)9798350388787;9798350388770

Motivated by the need of reducing the negative environmental impact of urban logistics, mainly coming from the steep increase of e-commerce, this article presents a solution for the effective monitoring of loading and unloading zones based on video recordings and their processing using Artificial Intelligence algorithms. Tailored for its implementation in urban environments, where available space is frequently scarce, the proposed solution is based on the application of two popular algorithms. Firstly, the object detection and classification algorithm YOLOv8 is employed to identify existing vehicles in the loading/unloading zones or in their immediate surroundings. Then, the object tracking algorithm DeepSORT is used, to monitor the presence of individual vehicles along time. This work focuses on assessing the performance of this combination of AI algorithms through a proof of concept consisting on two pre-recorded video sequences belonging to two different loading/unloading zones of different characteristics. The results obtained show the capability of YOLOv8 and DeepSORT to monitor use patterns, and therefore demonstrate its usefulness to support data-driven decisions regarding the regulation of these urban infrastructure assets.

关键词： vehicle detection computer vision YOLO DeepSORT load/unload zones

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Dynamic Neural Communication: Convergence of computer vision and Brain–computer Interface 13

Dynamic Neural Communication: Convergence of Computer Vision...

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13th International Winter conference on Brain-computer Interface, BCI 2025

作者： Park, Ji-Ha Lee, Seo-Hyun Kim, Soowon Lee, Seong-Whan Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human communication accompanies various features, such as articulatory movements, facial expressions, and internal speech, all of which are reflected in neural signals. However, most studies only generate short or fragmented outputs, while providing informative communication by leveraging various features from neural signals remains challenging. In this study, we introduce a dynamic neural communication method that leverages current computer vision and brain-computer interface technologies. Our approach captures the user’s intentions from neural signals and decodes visemes in short time steps to produce dynamic visual outputs. The results demonstrate the potential to rapidly capture and reconstruct lip movements during natural speech attempts from human neural signals, enabling dynamic neural communication through the convergence of computer vision and brain–computer interface. © 2025 IEEE.

关键词： Decoding

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Improving Power Precision in Hybrid Renewable Energy systems with a Quad Active Bridge DC-DC Converter and Neural Network-Based Decoupling 6

Improving Power Precision in Hybrid Renewable Energy Systems...

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6th International conference on Intelligent systems and computer vision, ISCV 2024

作者： Sabhi, Khalid Talea, Mohamed Bahri, Hicham Unversity Hassan II Department of Physics Casablanca Morocco

ISBN: (纸本)9798350350180

This paper presents the outcomes of our recent revision on the configurations and controls of hybrid renewable energy systems, incorporating solar panels, a wind turbine, a battery, and a load. We utilize a DC-DC converter (quad active bridge) with a high-frequency transformer to interconnect these sources. This structure reduces the need for multiple voltage adaptation converters and enhances source protection through magnetic coupling. Control is achieved by manipulating phase shifts between transformer voltages to adjust power production based on load demands. Additionally, we introduce a neural network block derived from the real system to dynamically recalculate phase shifts and achieve desired power levels in each source or load, enabling open-loop system control. © 2024 IEEE.

关键词： Hybrid power

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Predicting Cardiovascular Disease: A Scoping Survey on different Datasets and DL/ML Models using ECG 6

Predicting Cardiovascular Disease: A Scoping Survey on diffe...

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6th International conference on Intelligent systems and computer vision, ISCV 2024

作者： Akouz, Nouhaila El Ghazi, Asmae Moutaouakil, Wassima Hamida, Soufiane Cherradi, Bouchaib Raihani, Abdelhadi Hassan II University of Casablanca EEIS Laboratory ENSET of Mohammedia Mohammedia Morocco ENSET of Mohammedia Hassan II University of Casablanca GENIUS Laboratory SupMTI of Rabat EEIS Laboratory Mohammedia Morocco Hassan II University of Casablanca 2IACS Laboratory ENSET of Mohammedia Morocco SupMTI of Rabat GENIUS Laboratory Rabat Morocco Hassan II University of Casablanca STIE Team CRMEF Casablanca-Settat EEIS Laboratory ENSET of Mohammedia Mohammedia Morocco

ISBN: (纸本)9798350350180

Nowadays, cardiovascular disease is the leading global cause of death. Predicting heart disease has become a complex endeavor, requiring both expertise and advanced knowledge. In recent years, Internet of Things (IoT) technology has found application in healthcare systems to gather sensor data for the diagnosis and prediction of heart disease. Ongoing research explores various automated methods for identifying these diseases, aiding both patients and professionals in treatment of Healthcare. This article conducts a review focused on disease identification, classification, and recognition using sensors of Electrocardiogram (ECG) signals, examining studies published between 2020 and 2023 across diverse scientific databases. The review provides both quantitative and qualitative analyses of numerous scientific papers, revealing several machine learning and deep learning models. Notably, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) emerged as the most frequently employed algorithms. The insights from this review can guide the development of systems promoting patient autonomy. © 2024 IEEE.

关键词： Convolutional neural networks

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