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

共 12594 条记录，以下是121-130 订阅

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Performance Comparison of K-SVD, Discriminative K-SVD, and Approximate K-SVD Dictionary Learning algorithms for Public Road Type Recognition

Performance Comparison of K-SVD, Discriminative K-SVD, and A...

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International conference on Signal processing and Communication (ICSC)

作者： Ashwin Rai Pubali De Saibal Ghosh Pritam Paral Amitava Chatterjee Business Analyst Deloitte Touche Tohmatsu India LLP Institute of Engineering and Management University of Engineering and Management Kolkata India Dept. of Electrical Engineering. Jadavpur University Kolkata India Dept. of Electrical Engineering. IIEST Shibpur Howrah India Electrical Engineering Department Jadavpur University Kolkata India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In today's world, having the ability to drive has become of significant importance, especially while traveling. As a consequence, the importance of studying road safety is rising. Hence, the importance of research in designing intelligent systems to aid drivers in providing comfort, security, and safety is also gaining much momentum in recent times. On the other hand, designing Dictionary learning (DL) algorithms in conjunction with sparse representation/coding has become a forerunner in machine learning based research problems in the current decade. Many such DL based sparse solutions have been effectively proposed for a variety of signal processing and image processing algorithms. In some of our earlier works, we have shown how various dictionary learning based algorithms and system solutions can be successfully developed for home automation and ambient assisted leaving problems. Taking our research efforts in DL algorithms further, in this work we show how DL algorithms can be effectively used to categorize four common road types: urban roads, highways, residential areas, and rural areas. The algorithms have been developed based on sensory signals acquired by a driver wearing smart glasses, while driving in those road conditions. The DL based system developed can help providing guidance to a driver during driving. Three prominent varieties of DL algorithms, called K-singular value decomposition (K-SVD) algorithm, approximate K-SVD algorithm, and discriminative K-SVD algorithm, have been successfully implemented here for a benchmark, open dataset available for this problem and their suitable effectiveness has been investigated in detail. Although all the three varieties of K-SVD algorithms showed encouraging performances under different recognition problems, the discriminative K-SVD algorithm, employing orthogonal matching pursuit (OMP) algorithm at its core for sparse coding purposes, emerged as the best overall performer.

关键词： Machine learning algorithms Roads Signal processing algorithms Matching pursuit algorithms Machine learning Signal processing Approximation algorithms Safety Vehicles Smart glasses

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Pavement Distress Detection Using image processing from Unmanned Aerial Vehicle Data

Pavement Distress Detection Using Image Processing from Unma...

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International conference on Innovative Methods and Practical Applications for Cognizant Transportation systems, IMPACTS 2023

作者： Manjusha, M. Rudhra, A. Sunitha, V. Department of Civil Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819772995

The scale of public roads, particularly highways, has rapidly increased in many nations over the last few decades. To increase the road’s usability and thus its social and economic benefits, road maintenance has received more attention lately. Road inspections were done manually, the old-fashioned way takes a lot of time, and work, and is subjective. For proper road maintenance, deteriorated roads must be repaired as quickly as possible. Therefore, there is an urgent need for high-performance, low-cost technology to identify road surface distress. Unmanned aerial vehicles (UAVs), on the other hand, are an exciting option for monitoring the status of roads. They have the benefits of great flexibility, cheap cost, and simple agility. The data was collected in a 5 km road stretch of NH-336, Tamil Nadu. In this study, some of the computer vision tools including OpenCV, CUDA, etc. are tried to detect and localize the distress. It includes the detection and classification of pavement distress by using Object detection algorithms like Faster RCNN, Yolov3, and Yolov4 were used for detection and classification of road distresses. The main focus is on the detection and classification of potholes and cracks. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real-time. The application of deep learning techniques in transportation engineering problems is very rare. This study makes an effort to use deep learning techniques in the classification of the distresses and localizing them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Deep Learning Based Crack Detection Method for Bridges

Deep Learning Based Crack Detection Method for Bridges

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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

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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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International conference on image processing and Capsule Networks, ICIPCN 2020

International Conference on Image Processing and Capsule Net...

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International conference on image processing and Capsule Networks, ICIPCN 2020

ISBN: (纸本)9783030518585

The proceedings contain 72 papers. The special focus in this conference is on image processing and Capsule Networks. The topics include: Developing a framework to identify potential firms for job assignment;developing a framework for trend prediction of stocks prices;machine learning based diagnosis of alzheimer’s disease;iot based vehicle (car) theft detection;emg signal classification with effective features for diagnosis;eye fatigue algorithm for driver drowsiness detection system;an analytical approach for recognizing the occupational gender bias in the data systems;development of an algorithm for vertebrae identification using speeded up robost features (surf) technique in scoliosis x-ray images;highly protective framework for medical identity theft by combining data hiding with cryptography;improving the prediction accuracy of asd using class imbalance mitigation technique;stock price prediction based on technical indicators with soft computing models;credit card fraud detection technique based on hybrid classification;improved fast block matching motion estimation using multiple frames;efficient framework for identification of soybean disease using machine learning algorithms;brain image visualization;a framework for degraded kannada character recognition;classification of soybean diseases using pre-trained deep convolutional neural networks;feedforward back propagation neural network (ffbpnn) based approach for the identification of handwritten math equations;comparison of machine learning algorithms for smart license number plate detection system;ritchie’s smart watch data analytics and visualization;sentiment analysis for product rating using classification;face recognition system using a hybrid scale invariant feature transform based on local binary pattern;sign language identification using image processing techniques;foreword;preface.

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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...

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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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Identification of Human image Mapped with Clothing for Dress Selection

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

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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

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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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An Enhanced Pre-processing Technique For Eye image Segmentation

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

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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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