检索结果-内蒙古大学图书馆

4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. This paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. The hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. The results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. Therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： Computer-Aided Diagnosis Diabetic Retinopathy Machine learning Deep learning Ensemble learning image processing

来源：评论

学校读者我要写书评

暂无评论

Level Set Method Based on Molecular Beam Epitaxy Equation for image Segmentation 8th

Level Set Method Based on Molecular Beam Epitaxy Equation f...

引用

8th International Scientific conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Kuang, Di Guo, Zhichang Song, Fanghui School of Mathematics Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9783031774102

image segmentation is an important and complex task in computer vision. The variational level set method has become a popular approach for image segmentation due to its topological invariance. However, the evolution process may be unstable, leading to segmentation failures due to excessively flat or sharp final evolutionary profiles. In order to improve the accuracy and stability of evolution, we propose a high-order level set variational model, which applies the variational form of the molecular beam epitaxy (MBE) equation. The model avoids the need for re-initialization. In order to calculate the model, we drive the gradient flow of the model. We propose a finite difference semi-implicit and semi-explicit scheme. Additionally, we also design the scalar auxiliary variable scheme of the model, which expands its application, and can be solved by fast algorithms. Numerical experiments show that the model is outperforms current mainstream models in the processing of noisy images and medical images, and can be applied to actual image segmentation problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Molecular beam epitaxy

来源：评论

学校读者我要写书评

暂无评论

Non Homogeneous Realistic Single image Dehazing

Non Homogeneous Realistic Single Image Dehazing

引用

23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Vinay, P. Abhisheka, K. S. Shetty, Lithesh Kushal, T. M. Shylaja, S. S. PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350320565

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be dehazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.

关键词： Computer vision image color analysis conferences Moisture Object detection Computer architecture Real-time systems

来源：评论

学校读者我要写书评

暂无评论

YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

引用

2024 IEEE International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

Techniques for Pre-processing CT Angiography (CTA) images for the Diagnosis of Cardiac Disease 2

Techniques for Pre-processing CT Angiography (CTA) Images fo...

引用

2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Punitha, T. Santhi Preethika, S K. Piramu Vistas School of Computing Sciences Chennai India

ISBN: (纸本)9798350325799

Artificial Intelligence (AI) is a rapidly developing discipline that concentrates on teaching computers to comprehend and scrutinize images, particularly in the medical field, with a specific emphasis on diagnosing Cardiovascular Diseases. Coronary Computed Tomography Angiography (CCTA) is a widely accepted non-invasive diagnostic examination used to evaluate cardiac disease (CD). CCTA images are coronary artery anatomy, whereas functional stress testing assesses for inducible cardiac ischemia. Nevertheless, CCTA images frequently encounter diverse forms of noise and artifacts that can deteriorate their quality and hinder precise diagnosis. Preprocessing CT Angiography images is a vital undertaking in medical diagnosis utilizing computer vision. This study commences by employing image enhancement preprocessing methods to diminish noise, blurring, and image weakening. The initial stages involve utilizing edge detection algorithms and smoothing/filtering functions to enhance pixel clarity and edge definition in CCTA images using Python programming language and the PyCharm Integrated Development Environment (IDE) are implemented in this work. © 2023 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Invisible Backdoor Attack with image Contours Triggers 24th

Invisible Backdoor Attack with Image Contours Triggers

引用

24th International conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Hou, Yangzhi Yang, Ying Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819615445

In recent years, backdoor attack techniques on neural networks have been widely studied and researched. In this attack mode, the model implanted with a backdoor behaves normally when processing normal inputs, but once it encounters a special trigger designed by the attacker, its prediction results will be maliciously manipulated. Different backdoor attack strategies have been proposed to improve the stealth and robustness of the triggers. However, most of the existing backdoor attack strategies use fixed triggering patterns or input-independent triggering patterns, and thus are easily detected and blocked by existing defense systems. To address these limitations, we propose a backdoor attack method based on image contours. Specifically, we first use image contours as the target region for backdoor injection and fill them with specific color information to generate trigger patterns. Then, using our designed Generative Network (GN) and the Auxiliary Extraction Network (AE), the trigger patterns are covertly embedded into the images to be contaminated to generate a poisoned dataset, which contaminates the training model for the purpose of malicious attacks. Compared with traditional methods, our trigger patterns are more natural and harder to detect. Experimental results show that our proposed attack method achieves excellent performance in terms of both stealth and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Realtime Detection of Humps and Potholes

Realtime Detection of Humps and Potholes

引用

2023 International conference on Smart systems for Applications in Electrical Sciences, ICSSES 2023

作者： Tejas, B.S. Pavan, V. Rohith, H. Pranam, J. Hegde, Veena N. Bms College of Engineering Dept. of Electronics and Instrumentation Engineering Bengaluru India

ISBN: (纸本)9798350347296

Hump and pothole detection is essential for ensuring road safety and preventing damage to vehicles. In recent years, there has been a growing interest in developing automated methods for hump and pothole detection. This paper presents the detection of humps and potholes using techniques of image processing, machine learning and sensor-based approach. The proposed method involves the use of cameras and sensors to collect data on road conditions, analyze the data and identify areas with humps or potholes. Machine learning algorithms are applied learn to recognize patterns in the data and make accurate predictions. The solution provided is robust with an accuracy of 90% approximately in achieving the objective under different lighting and weather conditions. The proposed solution is implemented in real time and tested for different conditions. The analysis is carried out in terms of improved road safety and reduced maintenance costs using the proposed solution. © 2023 IEEE.

关键词： image processing

来源：评论

学校读者我要写书评

暂无评论

An application of smart visual air conditioning controller to enhance control efficiency in office environments

引用

RESULTS IN ENGINEERING 2025年 25卷

作者： Tian, Yu Zhao, Zilong Loo, Junn Yong Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Informat Technol Jalan Lagoon Selatan 47500 Bandar Sunway Selangor Darul Malaysia

Innovative solutions for sustainability and energy efficiency are crucial in green building management. This study presents a novel approach to optimizing air conditioning (AC) system operations in commercial buildings, with a focus on real-time control aimed at reducing energy consumption. We propose the Smart Visual Air Conditioning Controller (SVACC), which utilizes computer vision and deep learning-based human detection to intelligently manage AC operation, minimizing unnecessary runtime. By detecting human presence in meeting rooms, the system dynamically adjusts AC activation based on occupancy, thereby significantly reducing energy waste. A statistical analysis conducted over five months across ten conference rooms demonstrated that the SVACC reduced AC usage time by 33.60 %. We validate and optimize the SVACC across various building types, including commercial office spaces, industrial warehouses and laboratories, and residential apartments. The system achieved an optimal balance with 96.55 % precision and 93.33 % recall, resulting in an F1 score of 0.9492, demonstrating high performance across various environments. Our results underscore the effectiveness of the SVACC, which highlights the potential of integrating advanced deep learning models with HVAC systems to optimize energy consumption. This approach offers a promising solution for improving HVAC design and energy management across diverse building environments. Future work will focus on refining sensor technology and control algorithms to further optimize energy efficiency.

关键词： HVAC Building management Deep learning image processing Computer vision

来源：评论

学校读者我要写书评

暂无评论

Detection of Indian Fake Currency using image processing

Detection of Indian Fake Currency using Image Processing

引用

2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Asha Banu, S.M. Sandhya, S. Sundari, T Vijaya Shri Ranjani, P.R. Dept of ECE Sri Krishna College of Engineering and Technology India

ISBN: (纸本)9781665489621

The fake currency notes are detected using image processing employing MATLAB in this paper. This project aims in providing the best techniques in image acquisition, and image segmentation. The work uses CANNY's algorithm to extract the notes' features much more effectively. algorithms for image processing are used to extract the options. The technique used here functions well with the recently introduced denominations of 500 and 2000. The recommended strategy provides a practical means of detecting fake currency that is supported by physical inspection. © 2022 IEEE.

关键词： Fake detection

来源：评论

学校读者我要写书评

暂无评论

An improved YOLOv8-based vehicle detection model for rainy weather conditions 9

An improved YOLOv8-based vehicle detection model for rainy w...

引用

9th International conference on Computer and Communication systems (ICCCS)

作者： Lin, Dapeng Qu, Licheng Yue, Wenqi Wang, Jian Yu, Yanjie Changan Univ Sch Transportat Engn Xian Peoples R China Changan Univ Sch Informat Engn Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

In light of the limited detection accuracy and susceptibility to missed detections exhibited by most algorithms under rainy conditions, a rain-day vehicle target detection model based on improved YOLOv8 is proposed. Firstly, PIGWM is used to preprocess the original image for rain removal, and parameter importance-guided weight modification is employed to adjust network weights to address the performance degradation issue of deep learning models when processing incremental datasets, thereby improving the rain removal performance of images. Then, SlideLoss sliding loss function is introduced to enable the model to adaptively learn the threshold parameters of positive samples and negative samples, solving the imbalance problem between different samples and enhancing detection accuracy. Finally, CPCA attention mechanism is incorporated into the Neck feature fusion network to enhance the model's feature fusion capability. Experimental results on the self-built KITTI-RAIN dataset show that the improved algorithm achieves higher accuracy compared to the original model, with accuracy increasing from 92.6% to 94.5%, recall increasing from 82.9% to 87.6%, average precision increasing from 91.4% to 94.1%, and P, R, mAP increasing by 1.9%, 4.7%, and 2.7% respectively, demonstrating its effectiveness in adapting to vehicle detection tasks in rainy conditions.

关键词： yolov8,loss function image de-raining,feature fusion attention mechanism

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：