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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

共 12927 条记录，以下是131-140 订阅

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Deep Learning Based Crack Detection Method for Bridges

Deep Learning Based Crack Detection Method for Bridges

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Qinxue Wu Ruida Han Chang'an University Xi'an China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

In this paper, we focus on the problem of bridge crack detection. In view of the characteristics of bridge cracks, such as the existence of multiple scales and shapes, and the interference of complex backgrounds and different illumination conditions on the surface of bridges, we select the bridge crack images covering different scales, types, and illumination conditions to construct a dataset, and then carry out the image enhancement processes, such as rotating, randomly erasing, and adjusting the brightness and contrast of the cracks. Three deep learning algorithms, ResNet18, MobileNetV2 and EfficientNetB0, are selected for the experiments, and the performance of the models is evaluated by comparing the F1 scores, recall rates, confusion matrices, and observed loss curves. The results show that the proposed data processing and deep learning modeling strategy is effective in bridge crack detection, and the classification effect reaches a high level, providing a feasible method for bridge crack detection.

关键词： Bridges Deep learning Power engineering Shape Lighting Interference Robustness Surface cracks image enhancement Surface treatment

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

引用

Emerging systems and Intelligent Computing (ESIC), International conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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In-Sensor Learning on Magnetic Tunnel Junction Devices Using the Forward-Forward Algorithm

In-Sensor Learning on Magnetic Tunnel Junction Devices Using...

引用

Sensors and Information Technology (ICSI), International conference on

作者： Jiayuan Wang Minhui Ji Liyuan Yang Yueguo Hu Mengchun Pan Jiafei Hu Peisen Li College of Intelligence Science and Technology National University of Defense Technology Changsha China School of Stomatology The Fourth Military Medical University Xian China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Aiming at the real-time processing requirements in edge computing devices, we construct in-sensor computing units based on magnetic tunnel junction (MTJ) arrays, which realizes in-situ computation of vector-matrix multiplication with parameters dynamically programmed to adapt to different tasks. Different from the previously used back-propagation (BP) algorithm, we innovatively introduce the forward-forward (FF) algorithm, which utilizes the positive and negative samples “Goodness” optimization mechanism to reduce the computational complexity. In the noisy image classification task, the FF algorithm reduces the training time by more than 90% compared to the BP algorithm and significantly improves the convergence speed when perfect classification results are achieved.

关键词： Training Heuristic algorithms Real-time systems Classification algorithms Computational efficiency Noise measurement Junctions Magnetic tunneling image classification Convergence

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Vehicle image Anomaly Detection Based on Improved K-Means Algorithm and High-Resolution Camera

Vehicle Image Anomaly Detection Based on Improved K-Means Al...

引用

Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Xiaoqin Yang Syazwina Alias Hui Wang FoEBEIT SEGi University Kota Damansara Selangor Malaysia College of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing Jiangsu China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This study proposes a vehicle image anomaly detection framework based on improved K-means algorithm and high-resolution camera, which provides a new idea for road safety construction from the image processing level. The main innovation of the study contains 4 aspects: 1. A new vehicle target extraction algorithm based on deep learning is proposed. The algorithm uses the YOLOv5s network structure and combines the SimAM attention mechanism to optimize the model while improving the detection accuracy of vehicle targets. 2. A visual-based traffic flow clustering analysis is performed. This analysis achieved effective clustering of traffic flow through deep HOG feature extraction and parallel K-means clustering algorithm. 3. An optimized license plate detection and recognition algorithm based on YOLOv5 is proposed. The algorithm improves the accuracy of license plate recognition by adding the key point detectors. 4. The traditional K-means algorithm is optimized. The optimization idea is mainly to combine quantum computing and space-time theory. This optimization improves the convergence speed and clustering effect of the algorithm. The results of comparative experiments show that the improved K-means algorithm converges faster in traffic flow clustering analysis, and its license plate recognition accuracy is significantly better than traditional algorithms (such as k-means and FCM).

关键词： YOLO Technological innovation Machine learning algorithms Accuracy Clustering algorithms Cameras Road safety License plate recognition Anomaly detection Optimization

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An Autonomous Rover for Weed Detection

An Autonomous Rover for Weed Detection

引用

Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Gadiyaram Veda Lakshmi Durga Haritha V Naga Prudhvi Raj Gona Dayakar Bandaru Sneha Mohammad Nafisa Gadiyaram Balaji Prabhakara Sai Phaneedra Department of EIE VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This project enhances agricultural productivity and promotes sustainable farming through an autonomous weed detection rover. Powered by a Raspberry Pi, the rover integrates a high-definition camera and advanced image processing algorithms to differentiate weeds from crops in real time using visual attributes like color, texture, and shape. Key components, including motor drivers and MOSFETs, enable efficient power management and autonomous navigation, reducing human intervention. The system significantly decreases manual labor by approximately 80 % while ensuring precise herbicide application, minimizing environmental impact. This scalable and efficient solution highlights the potential of intelligent automation in agriculture, fostering sustainable weed management and improving long-term crop yields.

关键词： Productivity Precision agriculture Visualization Accuracy image color analysis Manuals Real-time systems Chemicals Crop yield Farming

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Identification of Human image Mapped with Clothing for Dress Selection

Identification of Human Image Mapped with Clothing for Dress...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： D Prem Raja Kailasam Selvaraj A. Baranidharan Rajkumar Pandiarajan V. Sivakumar V.Baby Shalini Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With a significant increase in the fashion market, consumers can shop from the comfort of own home through e-shopping. However, one of the prominent disadvantages is lack of ability to try a garment before buying through Internet; customers may feel unsure about size and yet look. The selection of dress from the proposed system is made realistic and accurate by implementing image processing, machine learning, and AR. The first step to the process is to isolate the human figure from the input image by using feature extraction such as background subtraction, edge-detection and of body part. Once the human figure is isolated, the system is then in a position to place chosen gargets on the corresponding identified points on the human figure. This mapping process encompasses the placement of clothing item's outline and fabric feel on the figure and gender of the user.

关键词： Industries Scalability image edge detection Clothing Machine learning Prediction algorithms Feature extraction User experience Fabrics Internet

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Learning systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

引用

1st International conference on Intelligent systems in Computing and Communications, ISCComm 2023

作者： Shetty, Sudheer Ganesh, Devadiga Likhit kumar Poojary, Rahul R. Raviraj Tejas, R. Hegde, Nagaratna P. Vikkurty, Sireesha Department of ISE Sahyadri College of Engineering & Management Mangaluru Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of ISE Alvas Institute of Engineering and Technology Moodbidri Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of CSE Vasavi College of Engineering Telangana Hyderabad India

ISBN: (纸本)9783031756078

In an era dominated by the ubiquitous sharing of visual content on social media, the proliferation of fake photographs and digital fakes has emerged as a critical issue. This paper explores how fake images are detected with fusion of by various techniques of image processing, CNN, and machine learning, presenting an innovative approach to address the intricate challenge of identifying manipulated images. This paper gives insight on various approaches of image processing techniques and state-of-the-art machine learning algorithms used in a robust forgery image detection system. A comprehensive framework is proposed on various image forensic methods, ensuring the precise detection of fake images across diverse contexts. The paper also emphasizes the impact of countering the dissemination of fake photographs to preserve the credibility of digital media. This combination of image processing, CNN and machine learning propels the ongoing quest to uncover the truth behind the surge of digital fakes in today's digital landscape. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Design and implementation of interactive image measurement software based on Tkinter and PIL library 25

Design and implementation of interactive image measurement s...

引用

Proceedings of the 4th International conference on Computer, Artificial Intelligence and Control Engineering

作者： Xiao He Zhulin Wang Shaozheng Zhan National Key Laboratory of Strength and Structural Integrity Xi' an Shaanxi China Aircraft Strength Research Institute of China Xi' an Shaanxi China

ISBN: (纸本)9798400712647

In this paper, we design and implement a high-precision image processing and measurement software based on Python. The software utilizes the Tkinter and PIL libraries to build a graphical user interface and integrates a variety of commonly used image processing algorithms, including grayscale conversion, edge extraction, Gaussian blur, image sharpening, binarization, and median filtering, enabling effective image enhancement and segmentation. In addition, the software provides interactive measurement functionality, allowing users to draw rectangular regions on the image using the mouse and automatically calculate the dimensions of these rectangles. Measurement results can be stored, displayed, and managed, facilitating users in analyzing and processing the data. To evaluate the performance and reliability of the software, we conducted experiments on multiple datasets. The experimental results demonstrate that the software can accurately process images of different types and qualities and provide stable and reliable measurement results. Compared with the existing C-scan measurement software of current equipment, the software developed in this study has certain advantages in terms of functional integration, interactivity, and measurement accuracy. The design and implementation of this software can meet the need to replace the existing C-scan system software, while also providing valuable references and insights for the further development of intelligent and automated image analysis systems.

关键词： PIL

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An Enhanced Pre-processing Technique For Eye image Segmentation

An Enhanced Pre-Processing Technique For Eye Image Segmentat...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Satrughan Kumar Arun Kumar Deepti Sahu Computer Science and Engineering Sharda University Greater Noida U.P India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Early diagnosis is essential to controlling glaucoma, a progressive disorder that results in irreversible vision loss. Retinal fundus imaging has emerged as a crucial diagnostic technique for retinal disorders, such as glaucoma, in recent times. But because of the intricate vascular patterns and intricate architecture of the retina, it can be difficult to analyze these images appropriately. Areas of interest in fundus pictures are frequently found using image segmentation algorithms; however, this can be challenging. This study examines a variety of segmentation techniques, emphasizing both conventional and cutting-edge ways for splitting retinal fundus pictures. It also covers assessment metrics that are used to evaluate how well segmentation algorithms perform in identifying minute structural alterations associated with glaucoma. The study also looks at how deep learning algorithms can be implemented to separate optic disc and cup. Deep learning models typically perform worse with larger datasets, even though they have demonstrated good accuracy with smaller datasets. The investigation underscores the need for more sophisticated deep learning methodologies and efficient segmentation techniques, as it shows that effectively segmenting the optic disc (OD) and cup (OC) remains a considerable challenge, particularly for large and complex datasets.

关键词： Deep learning Glaucoma Measurement Learning systems image segmentation Accuracy Refining Lighting Retina Optical imaging

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Construction Waste image Classification Algorithm Based on Fusion of Multiple Features

Construction Waste Image Classification Algorithm Based on F...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Yi You Boon Cheong Chew Institute of Technology Management and Entrepreneurship Universiti Teknikal Malaysia Melaka Malacca Malaysia Construction College Taizhou Vocational & Technical College Taizhou China Faculty of Technology Management and Technopreneurship Universiti Teknikal Malaysia Melaka Malacca Malaysia

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The rapid development of global urbanization has triggered the rapid expansion of the construction industry. The consequent construction waste problem has caused serious pollution to the environment and occupied a large amount of valuable land resources. In this context, effective classification and treatment of construction waste has become particularly urgent, which can help reduce environmental burdens and promote sustainable economic development. This study proposes a fine-grained construction waste classification algorithm based on image processing technology. The algorithm achieves automatic classification through a multi-feature fusion model and can accurately distinguish five major types of construction waste, including concrete, bricks, metal, wood, and plastic. This study further explores the impact of spatial positional relationships, semantic correlation, and internal structure analysis on improving classification accuracy. These research results not only provide technical support for the effective processing of construction waste, but also provide new methods and perspectives for other fine-grained image classification tasks. Experimental results prove that the algorithm proposed in this study significantly improves the classification accuracy. Specifically, the algorithm achieved high accuracy of 98.97%, 98.69%, 98.70%, 98.72% and 98.68% respectively in the classification tasks of concrete, bricks, metal, wood and plastic, which is significantly better than those based on CNN and enhanced FCM. Although these models are close to the performance of the algorithm in this study in some cases, the overall accuracy is lower.

关键词： Accuracy Urban areas Semantics Metals Stability analysis Real-time systems Classification algorithms Plastics Reliability image classification

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