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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent Learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International Conference on Artificial Intelligence, MICAI 2024

作者： López-Lobato, Adriana-Laura Acosta-Mesa, Héctor-Gabriel Mezura-Montes, Efrén Artificial Intelligence Research Institute University of Veracruz Campus Sur Calle Paseo Lote II Sección Segunda No. 112 Nuevo Xalapa Veracruz Xalapa-Enríquez91097 Mexico

ISBN: (纸本)9783031838811

Convolutional decision trees (CDTs) are machine learning models employed as explicable methods for image segmentation. This is because their graphical structure makes it relatively straightforward to interpret how the tree successively divides the image pixels into two classes, distinguishing between objects of interest and the image’s background. Several techniques have been proposed for the induction of CDTs. However, these techniques often require significant computational time and memory, with some requiring days to complete the induction process. This study proposes two techniques for selecting a representative sample of pixels from an image for the model’s training process: raw selection and median selection. These techniques aim to reduce the computational cost of inducing a CDT while maintaining or improving the resulting segmentation’s precision, measured by the F1-score. The proposed techniques were evaluated using the retinal vessel segmentation database and the SHADE-CDT method to induce a CDT, demonstrating a 70% reduction in processing time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Pixels

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

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International image processing, applications and Systems Conference (IPAS)

作者： Norah A. AlSubaie Ghayda A. AlMalki Ghada N. AlMutairi Sarah A. AlRumaih Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.

关键词： Measurement Location awareness Computer vision Urban planning machine learning Inspection Road safety Maintenance Mobile applications Defect detection

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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Using of AI Supported Computer vision on UAV’s*

Using of AI Supported Computer Vision on UAV’s*

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Human-Computer Interaction, Optimization and Robotic applications (HORA), International Congress on

作者： Alpaslan Durmuş Abdulhamit Sevgi Ahmet Murat Kadioğlu Erol Duymaz Department of Unmanned Aerial Vehicles and Operatorship Vocational Education School OSTİM Technical University Ankara Turkey Department of Mechatronic Vocational Education School OSTİM Technical University Ankara Turkey Internation Relationship Office OSTİM Technical University Ankara Turkey Ankara Turkey Department of Biomedical Device Tech Vocational Education School OSTİM Technical University Ankara Turkey

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Computer vision (CV) provides computer systems with the ability to perceive, analyze and understand the content of images and videos. Computer vision systems are used in many different areas, from medical diagnosis to industrial quality control. Different technologies and techniques are used to extract relevant information from a large number of images obtained through CV and machine vision (MV) components. The use of artificial intelligence-supported computer vision applications in unmanned aerial vehicles has significantly contributed to the use of UAV systems in different missions. The expansion of the application areas of unmanned aerial vehicles has increased the need for cameras and detection algorithms to be used for diagnosis and detection. Developing UAV systems and artificial intelligence technologies have made significant contributions to the use of UAV systems in defense, search and rescue in disasters, reconnaissance and observation, and also agricultural applications. image processing and artificial intelligence techniques include the capture, digitization and improvement of images step by step with image processing methods. In the study, a road map was created by examining the application of image processing techniques to a fixed-wing unmanned aerial vehicle, the development of its algorithms and the reactions of the unmanned aerial vehicle to this method.

关键词： Training Computer vision Visualization Atmospheric modeling Object detection Autonomous aerial vehicles Real-time systems Artificial intelligence Vehicle dynamics Videos

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Radar image processing Application Based on Space Cloud Computing in Basketball Game Guidance Camera

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machine Graphics and vision 2025年第2期34卷 3-27页

作者： Song, Jun School of Physical Education Qilu Normal University Ji’nan China

Capturing and presenting exciting moments is crucial for the audience’s experience in basketball game broadcast cameras. However, traditional radar image processing techniques are limited by various factors and cannot meet the demands of modern audiences for high quality, multi angle, and real-time performance. In response to these challenges, an innovative radar image processing system based on space cloud computing has been proposed. Compared with traditional radar image processing systems, the system proposed by the research institute had the best performance, with accuracy, recall, and F1 value reaching 97.08%, 96.88%, and 97.11%, respectively, and a transmission time of only 2.2 seconds;and the stability was greater than 90%, which was about 10% to 25% higher than other systems. In summary, the system proposed by the research institute has brought revolutionary improvements to basketball game guidance and filming through its efficient processing capabilities, accurate image recognition, fast data processing and transmission, and excellent stability. This not only greatly enriches the audience’s viewing experience, but also opens up new directions for the development of sports event broadcasting technology. With the further maturity of technology and the continuous expansion of applications, it is expected that this system will play a more important role in future sports event broadcasting, promoting the development of the entire industry towards higher quality and efficiency. © 2025 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Photointerpretation

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Artificial intelligence in healthcare applications targeting cancer diagnosis—part I: data structure, preprocessing and data organization

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Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 2025年第1期140卷 79-88页

作者： Araújo, Anna Luíza Damaceno Sperandio, Marcelo Calabrese, Giovanna Faria, Sarah S. Cardenas, Diego Armando Cardona Martins, Manoela Domingues Saldivia-Siracusa, Cristina Giraldo-Roldán, Daniela Pedroso, Caique Mariano Vargas, Pablo Agustin Lopes, Marcio Ajudarte Santos-Silva, Alan Roger Kowalski, Luiz Paulo Moraes, Matheus Cardoso Head and Neck Surgery Department University of São Paulo Medical School São Paulo State of São Paulo Brazil Hospital Israelita Albert Einstein São Paulo Brazil Department of Oral Medicine and Pathology Faculdade São Leopoldo Mandic Research Institute São Paulo Campinas Brazil Institute of Science and Technology (ICT-UNIFESP) Federal University of São Paulo São Paulo São José dos Campos Brazil Heart Institute University of São Paulo São Paulo State of São Paulo Brazil Department of Oral Pathology School of Dentistry Federal University of Rio Grande do Sul RS Porto Alegre Brazil Faculdade de Odontologia de Piracicaba Universidade Estadual de Campinas (FOP-UNICAMP) São Paulo Piracicaba Brazil Department of Head and Neck Surgery and Otorhinolaryngology A.C. Camargo Cancer Center São Paulo State of São Paulo Brazil

Background: machine learning techniques hold significant potential to support the diagnosis and prognosis of diseases. However, the success of these approaches is heavily dependent on rigorous data acquisition, preprocessing and data organization. Methods: This article reviews the literature to evaluate key factors in dataset construction, focusing on data structure, preprocessing, and data organization, particularly in the context of imaging data. Results: The main issues with data construction when dealing with medical applications are noise (incorrect or irrelevant data), sparsity/ limited availability, representativeness/variability, and data imbalance (uneven class distribution).While preprocessing steps prepare the data to be suitable for the models, data organization focuses in improving data arranging to increase the model performance. Additionally, the impact of CNN complexity in processing balanced, imbalanced, and complex datasets shows that complex CNNs are not always the optimal choice for every classification problem. Conclusion: By integrating knowledge from Health Sciences and Biomedical Engineering, we aim to enhance healthcare professionals' understanding of machine learning for image analysis in Oral Medicine and Pathology. This encourages their involvement in patient recruitment and data acquisition, broadening their roles and significantly contributing to the creation of well-characterized datasets for future research and applications. © 2025 Elsevier Inc.

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Decoding machine Learning Algorithms for DR Detection

Decoding Machine Learning Algorithms for DR Detection

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagrit Ahuja Varun Sharma Raghavv Gupta Preeti Kapoor Shaveta Arora Bharati Vidyapeeth Institute of Management and Research New Delhi India Department of Computer Science and Engineering The NorthCap University Gurugram India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Diabetic retinopathy is a serious eye disease which can lead to vision defects in diabetic patients. Early detection is important for preventing vision loss. Automating the detection process makes it less laborious and helps in achieving more accurate results. Therefore, this research aims to study various machine learning methods and develop an effective model for diabetic retinopathy detection. The proposed model operates through four stages: pre-processing, feature extraction, feature optimization, and classification. image processing techniques are thoroughly utilized to enhance the fundus images. The textual and spatial features are then extracted and most relevant features are selected using particle swarm optimization. These features are then fed into machine learning classifiers for disease diagnosis. The model is trained and validated on the MESSIDOR I and MESSIDOR ii datasets. The model achieves an accuracy (85.9%), F1 score (0.8585), Sensitivity (0.8049) and Specificity (0.8958) on MESSIDOR ii data with Support Vector machine (SVM) classifier. The results obtained show the effectiveness of the work in early detection.

关键词： Support vector machines Deep learning Diabetic retinopathy Accuracy machine learning algorithms Nearest neighbor methods Feature extraction Particle swarm optimization Optimization vision defects

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machine vision applications for Welfare Monitoring in Aquaculture: Challenges and Opportunities

AQUACULTURE, FISH AND FISHERIES

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AQUACULTURE, FISH AND FISHERIES 2025年第1期5卷

作者： Fitzgerald, Amy Ioannou, Christos C. Consuegra, Sofia Dowsey, Andrew de Leaniz, Carlos Garcia Swansea Univ Dept Biosci CSAR Swansea SA2 8PP Wales Univ Bristol Sch Biol Sci Bristol England Inst Invest Marinas IIM CSIC Vigo Spain Univ Bristol Bristol Vet Sch Bristol England Univ Vigo CIM Vigo Spain

Increasing consideration of welfare in aquaculture has prompted interest in non-invasive methods of monitoring that avoid unnecessary stress and handling. machine vision (MV) provides a potential solution to these needs, as it can be used for non-invasive monitoring of animal health and welfare in real-time. We examined the practical applications of MV for welfare monitoring in aquaculture, the hardware and algorithms used for automated data collection, and the main challenges and solutions for data processing and analysis. The most common application of MV has been the estimation of size-related metrics (growth, biomass) in fish, but key aspects of welfare, such as monitoring of parasites and disease or detection of stress-related behaviours, are lagging behind. Numerous camera setups have been used, ranging from single to stereoscopic cameras and from emersed to submerged cameras, but these have often been used under optimal conditions that may not always reflect those prevalent in industry (high densities, low visibility), likely overestimating performance. Object detection algorithms, such as YOLO, have been the approach of choice for most MV applications in aquaculture, but our review has identified an increasing number of alternatives that can help circumvent some of the challenges posed by high densities and poor lighting typical of commercial farms. MV has the potential to transform welfare monitoring in aquaculture, but there are still important challenges that need to be overcome before it can become mainstream, namely the ability to detect ectoparasites and diseases, identify abnormal behaviours, and work across taxa, particularly in crustaceans.

关键词： algorithm artificial intelligence computer vision fish farm non-invasive

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Current trends and future orientation in diagnosing lung pathologies:A systematic survey

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Intelligent Medicine 2025年第1期5卷 23-36页

作者： Tamim M.Al-Hasan Mohammad Noorizadeh Faycal Bensaali Nader Meskin Ali Ait Hssain Department of Electrical Engineering College of EngineeringQatar UniversityDohaP.O.Box 2713Qatar Medical Intensive Care Unit Hamad Medical CorporationDohaQatar

Lung diseases pose a significant threat to public health worldwide,resulting in a substantial number of *** such as chronic obstructive pulmonary disease and lung cancer constitute two of the three deadliest diseases worldwide,contributing to over 3 million deaths *** study offered a comparative analysis of different diagnostic techniques used for lung pathologies from an engineering *** review concentrated on intelligent detection methods,including electronic nose,computer vision(CV),or image processing,and biosensors such as graphene-field effect transistor(FET).The E-nose-based detection technique uses electronic sensors to recognize volatile organic compounds(VOCs)in the exhaled *** VOCs can aid in the diagnosis of lung pathologies such as *** CV processing method involves the application of advanced imaging techniques and machine learning algorithms to scrutinize and diagnose lung pathologies and ventilatorassociated pneumonia(VAP).Lastly,biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological *** information can be used to diagnose lung pathologies and *** study examined the current state-of-the-art methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering *** study underscored the potential of these techniques to enhance the diagnosis of lung pathologies and VAP and presents the advances in the field of smart biomedical ***,it emphasized the necessity for further research to optimize their performance and clinical usefulness.

关键词： Biosensors Bio-signal processing Electronic nose Lung disease Nosocomial infections Ventilator-associated pneumonia

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Control of Technical Parameters of Industrial Products Through machine vision 43rd

Control of Technical Parameters of Industrial Products Throu...

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43rd IBIMA Conference on Artificial intelligence and machine Learning, IBIMA-AI 2024

作者： Gheorghita, Vlad National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (纸本)9783031774928

During the production process, a lot of undesirable or faulty products are also created. Since these products cannot be used or distributed on the market, they must be discarded. Product integrity and quality inspection, which is also a result of production process efficiency and optimization, is one of the most important procedures in modern industrial applications. The quickest and most efficient way to make sure products meet the quality requirements is through an automated verification process. These systems are considered as essential by some applications because replacing them with human employees would necessitate a substantial workforce and related costs. This research provides a mechanical experimental system that will be able to verify fast and control the most important parameters of various products using the artificial intelligence approach and machine vision techniques. The cost of the designed system is low and the structure used is clear. The demonstration of the utility of the created product was carried out on a toothed wheel with many essential technical characteristics, a widely used landmark in the field of manufacturing engineering. image processing techniques are used such as threshold function, calibration, pattern matching, measure. The experimental model designed is easy to implement and can represent a transition towards the application of modern industry 5.0 principles in different processing companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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