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

共 985 条记录，以下是81-90 订阅

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Macula Swelling Detection using Retinal Fundus Images

Macula Swelling Detection using Retinal Fundus Images

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2023 IEEE International conference on Imaging systems and Techniques, IST 2023

作者： Khan, Rukham Mumtaz Ali Khan, Fazal Saadat Khan, Fauzia Hassan, Rafia National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan University of Tartu Dept. of Computer Science Tartu Estonia Bahria University Dept. of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350330830

Diabetic Macular Edema (DME) is an advanced symptom of Diabetic Retinopathy (DR) and can lead to irreversible vision loss. Macular Edema (ME) is the swelling of the macula, the part of the eye accountable for thorough central vision. The utilization of image processing in the detection of diabetic macular edema (DME) plays a crucial role in the field of medical imaging, effectively reducing the time and manual effort required by medical specialists to identify this disease. In this paper, we proposed a method that uses digital fundus images to analyze the swelling of the macula. The proposed algorithm consists of four steps, i.e., (i) retinal image dataset collection, (ii) retinal image pre-processing, (iii) feature extraction, and (iv) classification to categorize the disease into three different stages, i.e., normal, stage 1 (mild) and stage 2 (severe). The proposed method is tested on three databases, i.e., MESSIDOR, DMED, and the local dataset AFIO obtained from the local hospital in Pakistan. We use three parameters to check the validity of the proposed algorithm, i.e., visual inspection, accuracy, and area under calculation time. Our method achieves an average accuracy of 96.07 % with a sensitivity of 93.74 % and specificity of 97.32%. We also compare our results with other proposed methods, which shows our method performs better. © 2023 IEEE.

关键词： Eye protection

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Infrared-based point cloud reconstruction for heat loss detection in a virtual reality environment 2

Infrared-based point cloud reconstruction for heat loss dete...

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conference on NDE 4.0, Predictive Maintenance, Communication, and Energy systems - The Digital Transformation of NDE ii

作者： Kulkarni, Nitin Nagesh Peretto, Lorenzo Bottalico, Fabio Niezrecki, Christopher Sabato, Alessandro Univ Massachusetts Dept Mech & Ind Engn 1 Univ Ave Lowell MA 01852 USA Politecn Torino Dept Control & Comp Engn DAUIN Corso Castelfidardo 34-D I-10138 Turin Italy

ISBN: (数字)9781510672116

ISBN: (纸本)9781510672116;9781510672109

Structure from Motion (SfM) is a photogrammetry technique with diverse applications, such as surveying, mapping, and inspection. It facilitates remote assessment of large systems by converting visible spectrum images into threedimensional (3D) point clouds. Recent advancements have extended SfM to employ infrared (IR) images, enabling the detection of issues such as water infiltration and sub-surface defects that can cause energy loss in a building. Combining IR-based SfM with unmanned aerial vehicle (UAV) technologies yields high-definition 3D point clouds that can be used in a virtual reality (VR) environment. This study showcases the application of the SfM-IR-UAV method to create 3D virtual models of selected buildings in the University of Massachusetts Lowell's campus to assess energy loss. The 3D virtual models are made accessible via a VR platform to develop a remote inspection and maintenance tool. The VR platform also holds the capabilities to mark abnormalities in the structure, which can later be used for informing renovation or repair. The proposed approach simplifies remote assessment, reducing costs and operational risks. While this research focuses on energy audits, its outcomes extend to diverse domains. Further development holds the potential to expedite nondestructive evaluation and enhance structural health monitoring in civil and mechanical engineering, utilizing the 3D point cloud thermal model within a VR environment.

关键词： computer vision Infrared Imaging Non-destructive Evaluation Structure from Motion Virtual Reality

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Meta-heuristics Applied to Multiple DG Allocation in Radial Distribution Network: A comparative study 5

Meta-heuristics Applied to Multiple DG Allocation in Radial ...

引用

5th International conference on Intelligent systems and computer vision, ISCV 2022

作者： Tarraq, Ali Elmariami, Faissal Belfqih, Abdelaziz Haidi, Touria Agouzoul, Naima Gadal, Rabiaa Casablanca Morocco EHTP Hassan II University Laboratory of Energy and Electrical Systems ENSEM Laboratory of Systems Engineering Casablanca Morocco

ISBN: (纸本)9781665495585

Due to the increasing electricity demand, the distribution network is becoming more and more uncontrollable and subject to higher power losses. To cope with this problem, the optimal integration of distributed generators (DGs) is proving to be efficient and sustainable. In this context, this paper investigates the minimization of active losses and the improvement of voltage profile through the integration of Multiple DGs in the IEEE 33-bus radial distribution system (RDS). The study aims to determine the optimal locations and sizes of l to 7 DGs to be integrated, in the case of unity power factor (UPF-DG) and optimal power factor (OPF-DG). The results are evaluated in a comparative study between three meta-heuristic optimization methods, namely Improved Cuckoo Search Algorithm (ICCSA), Improved Grey Wolf optimizer (IGWO), and a Chaotic-based Neural Network Algorithm (CNNA). In summary, CNNA outperforms the other algorithms mentioned above by increasing the problem dimension. Indeed, the total active loss reduction can reach 9S.27% by integrating seven OPF-DGs. On the opposite, poor results are generated by ICCSA. © 2022 IEEE.

关键词： Optimization

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Multi-Objective Evolutionary Algorithms for Hardware Design Space Exploration 4

Multi-Objective Evolutionary Algorithms for Hardware Design ...

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4th International conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kumar, Rahul Kapoor, Monit Varshney, Neeraj Shrivastava, Anurag Saraswat, Manish Singh, Devendra Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India GLA University Department of Computer Engineering and Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Lloyd Institute of Engineering & Technology Greater Noida India Uttaranchal University Uttaranchal Institute of Technology Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350307757

This work investigates the use of Multi-Objective Evolutionary Algorithms (MOEAs) in the Hardware Design Space Exploration context. Through the use of a heterogeneous data set that spanned different hardware configurations and performance measurements, four widely known MOEAs namely NSGA-ii, SPEA2 IBEA MOPSO have been systematically assessed. Experiments have been aimed at major indicators, including convergence, diversity, and hypervolume maximization. It has been shown that NSGA-ii and SPEA2 have been largely well-balanced in terms of convergence versus diversity, a feature that makes them suitable options for the optimization task. IBEA has been best in hypervolume maximization, which indicates that it is a powerful method when applied to scenarios where extensive coverage of the design space needs to be achieved. MOPSO, using the ideas from particle swarm optimization, gave a solid competitor that featured excellent convergence and diversity. This work also does a comparative study with related works, which shows the generality of MOEAs in different domains such as facility location optimization, system design and control theory robotics bioinformatics computer vision, and networked systems. The research provides relevant inputs to help researchers and practitioners choose the best algorithms for hardware design optimization, which includes considering specific goals and constraints. Consequently, the research shows us how this way and a method of creation. © 2024 IEEE.

关键词： Multiobjective optimization

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Adversarial Branch Architecture Search for Unsupervised Domain Adaptation 22

Adversarial Branch Architecture Search for Unsupervised Doma...

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22nd IEEE/CVF Winter conference on Applications of computer vision (WACV)

作者： Robbiano, Luca Rahman, Muhammad Rameez Ur Galasso, Fabio Caputo, Barbara Carlucci, Fabio Maria Politecn Torino Turin Italy Sapienza Univ Rome Rome Italy Politecn Torino CINI Consortium Turin Italy Huawei Noahs Ark Lab London England

ISBN: (纸本)9781665409155

Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date, all proposed approaches rely on human expertise to manually adapt a given UDA method (e.g. DANN) to a specific backbone architecture (e.g. ResNet). This dependency on handcrafted designs limits the applicability of a given approach in time, as old methods need to be constantly adapted to novel backbones. Existing Neural Architecture Search (NAS) approaches cannot be directly applied to mitigate this issue, as they rely on labels that are not available in the UDA setting. Furthermore, most NAS methods search for full architectures, which precludes the use of pre-trained models, essential in a vast range of UDA settings for reaching SOTA results. To the best of our knowledge, no prior work has addressed these aspects in the context of NAS for UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search for UDA (ABAS): i. we address the lack of target labels by a novel data-driven ensemble approach for model selection;and ii. we search for an auxiliary adversarial branch, attached to a pre-trained backbone, which drives the domain alignment. We extensively validate ABAS to improve two modern UDA techniques, DANN and ALDA, on three standard visual recognition datasets (Office31, Office-Home and PACS). In all cases, ABAS robustly finds the adversarial branch architectures and parameters which yield best performances. https://***/lr94/abas.

关键词： Bridges Visualization computer vision Adaptation models computer architecture Picture archiving and communication systems Standards

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A Practical Approach for Vehicle Speed Estimation in Smart Cities 1

引用

8th Annual International conference on Machine Learning, Optimization and Data science (LOD)

作者： Barra, Silvio Carta, Salvatore Meloni, Antonello Podda, Alessandro Sebastian Recupero, Diego Reforgiato Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy Univ Cagliari Dept Math & Comp Sci Cagliari Italy

ISBN: (数字)9783031255991

ISBN: (纸本)9783031255984;9783031255991

The last few decades have witnessed the increasing deployment of digital technologies in the urban environment with the goal of creating improved services to citizens especially related to their safety. This motivation, enabled by the widespread evolution of cutting edge technologies within the Artificial Intelligence, Internet of Things, and computer vision, has led to the creation of smart cities. One example of services that different cities are trying to provide to their citizens is represented by evolved video surveillance systems that are able to identify perpetrators of unlawful acts of vandalism against public property, or any other kind of illegal behaviour. Following this direction, in this paper, we present an approach that exploits existing video surveillance systems to detect and estimate vehicle speed. The system is currently being used by a municipality of Sardinia, an Italian region. An existing system leveraging Convolutional Neural Networks has been employed to tackle object detection and tracking tasks. An extensive experimental evaluation has been carried out on the Brno dataset and against state-of-the-art competitors showing excellent results of our approach in terms of flexibility and speed detection accuracy.

关键词： Surveillance systems Convolutional neural networks Object detection Smart cities

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A New Quantum CNN Model for Image Classification 8th

A New Quantum CNN Model for Image Classification

引用

8th International conference on Computing, Control and Industrial Engineering, CCIE 2024

作者： Zhao, Xingqiang Chen, Tianlong School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Science and Technology Service Zhejiang Rural Commercial United Bank Co. Ltd. Hangzhou China

ISBN: (纸本)9789819769360

Quantum density matrix represents all the information of the entire quantum system, and novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and linguistic ambiguity. Earlier work has already demonstrated potential quantum advantage for natural language processing (NLP) in a number of manners. But as far as we know, the idea of the quantum density matrix has had little application in the field of computer vision (CV). Inspired by the quantum density matrix, we argue that the quantum density matrix can enhance the image feature information and the relationship between the features for the classical image classification. Specifically, (i) we combine density matrices and CNN to design a new mechanism;(ii) we apply the new mechanism to some representative classical image classification tasks. A series of experiments show that the application of quantum density matrix in image classification has the generalization and high efficiency on different datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection 38

Textual Training for the Hassle-Free Removal of Unwanted Vis...

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38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT. © 2024 Neural information processing systems foundation. All rights reserved.

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Single-Image Haze Reduction Using a Straightforward Additive Model with a Haze Smoothness

Single-Image Haze Reduction Using a Straightforward Additive...

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2023 International conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sundari, K. Jebastin, K. Dhinakaran, K. II M.E-Communication Systems PSN College of Engg Tech Tirunelveli India Assistant Professor PSN College of Engg Tech Tirunelveli India Research Scholar Bangalore India

ISBN: (纸本)9798350335835

A difficult task in computer vision is recovering the clear form of a hazy image via single-image haze reduction. To replicate the production of hazy pictures, this study suggests an additive haze model. Contrasts are made using the established visual paradigm, which sees the haze as an extra covering over asharp image. Medium transmission rates or global ambient light are not estimated by the model. Additionally, a haze smoothness threshold is suggested to limit this model based on the crucial fact that haze fluctuates gently and smoothly throughout the image. The clean image is substantially smoother than the hazy layer, according to this study. With this, a clean image and a single blurry image can be easily distinguished. The suggested method outperforms cutting-edge single-image haze removal methods shown by experimental findings and comparisons to artificial and live videos. © 2023 IEEE.

关键词： Additives

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Neural Substitution for Branch-Level Network Re-parameterization 17th

Neural Substitution for Branch-Level Network Re-parameteriz...

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17th Asian conference on computer vision, ACCV 2024

作者： Oh, Seungmin Ryu, Jongbin Department of Artificial Intelligence Ajou University Suwon Korea Republic of Department of Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9789819609659

We propose the neural substitution method for network re-parameterization at the branch-level connectivity. This method learns different network topologies to maximize the benefit of the ensemble effect, as re-parameterization allows for the integration of multiple layers during inference following their individual training. Additionally, we introduce a guiding method to incorporate non-linear activation functions into a linear transformation during re-parameterization. Because branch-level connectivity necessitates multiple non-linear activation functions, they must be infused into a single activation with our guided activation method during re-parameterization. Incorporating the non-linear activation function is significant because it overcomes the limitation of the current re-parameterization method, which only works at block-level connectivity. Restricting re-parameterization to block-level connectivity limits the use of network topology, making it challenging to learn a variety of feature representations. On the other hand, the proposed approach learns a considerably richer representation than existing methods due to the unlimited topology, with branch-level connectivity, providing a generalized framework to be applied with other methods. We provide comprehensive experimental evidence for the proposed re-parameterization approach. Our code is available at https://***/SoongE/neural_substitution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Linear transformations

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