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

共 12624 条记录，以下是131-140 订阅

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

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International Workshop on Mobile Terahertz systems (IWMTS)

作者： Yevhen Ivanenko Aman Batra Viet Thuy Vu Mats I. Pettersson Thomas Kaiser Blekinge Institute of Technology Karlskrona Sweden University of Duisburg-Essen Duisburg Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325–0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm.

关键词： Terahertz wave imaging Radar remote sensing Accuracy Heuristic algorithms Signal sampling Approximation algorithms Radar polarimetry Reliability image reconstruction Remote sensing

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image Search Engine with Recognition

Image Search Engine with Recognition

引用

Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： JMSV Ravi Kumar Bellaganti Dheeraj Geddada Gowtham Dokala Vivek Babu Bonthu Asha Kiran M. Srikanth Department of Information Technology SRKR Engineering College Bhimavaram Andhra Pradesh India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use.

关键词： Deepfakes Visualization Sentiment analysis image recognition Face recognition Search engines Feature extraction User experience Artificial intelligence Usability

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A Lightweight and Efficient Convolutional Neural Network for Crowd Counting

A Lightweight and Efficient Convolutional Neural Network for...

引用

International image processing, Applications and systems conference (IPAS)

作者： Marios Constantinou Panayiotis Kolios Christos Kyrkou KIOS CoE University of Cyprus Nicosia Cyprus Department of Computer Science KIOS CoE University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The crowd counting task plays a key role in ensuring public safety during large gatherings events. Most prominent works in this area, use large and computationally demanding deep learning model architectures, which require substantial computational power, limiting their usage in a real-world scenario under resource constraints. In this work we consider the trade-off between the model’s predicted accuracy and computational speed. We propose an improved version of HR-Net, which is substantially smaller and faster than the original, but preserves its localization and counting performance. Through targeted removal of unnecessary modules and branches, we demonstrate an increase in frames-per-second by 37.71% on an Nvidia Jetson Orin, and a reduction of GMACs and parameters by 77.41% and 73.07% respectively, while retaining competitive localization and counting performance, specifically for aerial imagery scenarios. Our modifications enable the algorithm to process in real-time higher resolution images, which is crucial when dealing with small objects. Furthermore, because most crowd counting datasets contain random images gathered from the web, and limited aerial images of crowds, we introduce a specialized dataset of high-resolution aerial imagery for sparse and dense crowds in various environments, that contains new drone-captured annotated image data.

关键词： Location awareness Deep learning Limiting image resolution Computational modeling Computer architecture Predictive models Real-time systems Public security Inference algorithms

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Fourth International conference on image processing and Capsule Networks 1

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丛书名： Lecture Notes in Networks and systems

1000年

作者： Subarna Shakya João Manuel R. S. Tavares Antonio Fernández-Caballero George Papakostas

ISBN: (数字)9789819970933

ISBN: (纸本)9789819970926

This book includes high-quality research papers presented at the Fourth International conference on image processing and Capsule Networks (ICIPCN 2023), which is held in Bangkok, Thailand, during 10–11 August 2023. This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations.

关键词： Computational Intelligence image processing and Computer Vision Signal, image and Speech processing algorithms

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Optical Authenticity in Pushbroom System for Spectral Information Protection

Optical Authenticity in Pushbroom System for Spectral Inform...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Pablo Gomez Roman Jacome Emmanuel Martinez Hans Garcia Henry Arguello Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga Colombia Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga Colombia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access or modification before applying a protective strategy. In response, this work presents a pushbroom HSI system that integrates an authentication key on the acquisition process, ensuring the key imperceptibility without affecting performance in spectral classification tasks. In addition, this work develops an algorithm designed to simulate attacks through class modifications in the HSI. Additionally, a detection algorithm is proposed to identify modifications by comparing the signed HSI with the altered version, effectively recognizing the embedded key. The proposed method was tested using simulated data and acquired data in the optical laboratory. Results demonstrate that the system maintains classification accuracy, further enhancing imperceptibility. Additionally, modifications are detectable by the key with 99% accuracy.

关键词： Integrated optics Accuracy Authentication Signal processing algorithms Optical imaging Classification algorithms Optical sensors Optical signal processing Speech processing Hyperspectral imaging

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Pavement Distress Detection Using image processing from Unmanned Aerial Vehicle Data

Pavement Distress Detection Using Image Processing from Unma...

引用

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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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...

引用

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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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

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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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