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

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Patch based image processing for complex environment characterization

Patch based image processing for complex environment charact...

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International image processing, Applications and systems conference (IPAS)

作者： Corentin Menier Cyril Meurie Timothée Guillemaille Yassine Ruichek Juliette Marais Univ Gustave Eiffel COSYS-LEOST Villeneuve d’Ascq France UTBM Belfort France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

关键词： Training Global navigation satellite system Visualization Satellites Three-dimensional displays Accuracy Uncertainty Vegetation mapping Cameras Trajectory

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Automated Pomegranate Leaf Disease Identification: An image processing Perspectives

Automated Pomegranate Leaf Disease Identification: An Image ...

引用

International conference on Communication systems and Network Technologies (CSNT)

作者： B Meghana Janani J S Lalitha Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Agriculture is a predominant occupation in India, and to date, automated plant disease detection has been applied primarily to crops such as paddy, wheat, and various vegetables (e.g., tomato, cucumber, and potato). In this work, a novel approach is proposed for the automated detection and labeling of pomegranate leaf diseases by employing Support Vector Machine (SVM) for classification and K-Means Clustering for image segmentation. The study focuses on four specific plant diseases—Bacterial Blight, Alternaria Alternata, Cercospora Leaf Spot, and Anthracnose—as well as the detection of healthy leaves when no affected areas are present. To enhance the visibility of the leaf images, contrast enhancement is performed as a preliminary step. Subsequently, K-Means Clustering is applied to segment the input image in the Lab color space. A Multiclass SVM is then utilized to classify the segmented regions based on identified affected portions of the leaf. Finally, the Region of Interest (ROI) is determined to calculate and display the total affected area, facilitating precise disease-type detection. Experimental results indicate that the proposed method achieves an accuracy of 0.96, a precision of 0.95455, a recall of 0.9333, an F1 score of 0.978, and a kappa statistic of 0.947, thereby demonstrating the efficacy of the proposed system for automated pomegranate leaf disease detection.

关键词： Support vector machines image segmentation Plant diseases Machine learning algorithms image color analysis Clustering algorithms Crops Machine learning Labeling Diseases

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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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Sprinter Performance Evaluation System Based on Improved image Segmentation Method

Sprinter Performance Evaluation System Based on Improved Ima...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Haili Meng Chongqing Vocational Institute of Engineering Chongqing China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This project attempts to build a sprinter performance analysis system using optimized image segmentation technology, aiming to solve the limitations of traditional evaluation methods in dynamic blur and complex backgrounds. By introducing the improved AO-GrabCut algorithm, the image segmentation process is optimized, the separation quality of athletes and backgrounds is improved, and dynamic images of athletes moving at high speed on the track are collected through high-resolution cameras. The speed and stride of athletes are calculated in real time using the optical flow method to screen key attributes of the action. The experimental results show that the system can accurately capture the performance indicators of athletes such as starting time, acceleration time, and sprint time. The accuracy and recall rate have reached a high level, and it is outstanding in complex and changing situations. This tool provides an effective auxiliary means for the training and competition analysis of sprinters, and provides strong data support for sports science research.

关键词： Performance evaluation Training image segmentation Accuracy Heuristic algorithms Stability analysis Real-time systems Performance analysis Optimization Sports

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Garbage Detection System Using image processing

Garbage Detection System Using Image Processing

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Disruptive Technologies (ICDT), International conference on

作者： Aditya Pandey Praveen Kumar Rai Aditya Singh Aman Patwa Computer Science Engineering GL Bajaj Institute of Technology & Management Gr. Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This research presents a brand new development system for garbage detection using advanced techniques in image processing. This system is primarily to identify and then classify the waste in real time, thus tackling waste management problems. This has been done using Convolutional Neural Networks (CNNs) and image preprocessing to achieve high accuracy in the detection of different waste types such as plastics, metals, and organics. The experimental results prove the model's robustness across diverse environments that make it amenable to large-scale deployment. The work is meant to serve municipalities, industries, and environmental organizations for automating the waste segregation process [1], [2]. The paper deliberates about some image processing techniques on edge detection, segmentation, and feature extraction since these form the basis for waste identification and classification types that rely on them. Integration of such methods into machine learning and deep learning algorithms, especially using convolutional neural networks (CNN), makes the process more accurate and robust in detection. Also discussed in the review are the pre-processing methods, such as noise reduction and contrast enhancement, which improve the quality of input data to achieve better detection results.

关键词： Waste management Accuracy Reviews Noise reduction Metals Organizations Real-time systems Robustness Convolutional neural networks Plastics

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Data processing, analysis and Machine Learning of Electrical Atomic Force Microscopy (AFM) in Data Cube mode: conduction of a nanomaterial for Lithium-ion battery electrodes

Data processing, analysis and Machine Learning of Electrical...

引用

International image processing, Applications and systems conference (IPAS)

作者： Rosine Coq Germanicus Peter De Wolf Petr Klapetek Unicaen Ensicaen CNRS CRISMAT UMR6508 Normandie Univ Caen France Bruker Nano Surfaces Santa Barbara CA USA Czech Metrology Institute Brno Czech Republic

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

With the increasing integration of functional systems, nanoscale characterization has become crucial not only for material investigation but also for advancing the understanding of local behavior and optimizing performances. This paper details data processing techniques and machine learning methodologies for the analysis and prediction of advanced electrical Atomic Force Microscopy datasets. The datasets are acquired using a hyperspectral Data Cube approach, which combines both mapping and spectroscopy acquisitions. In the context of decarbonization and electrification, current challenges are related to energy storage and transport. A cathode material used in Lithium-ion battery electrodes is investigated for its electrical transport properties. To comprehensively characterize the electrical properties, the conductive mode of Atomic Force Microscopy is employed using the Data Cube method, which enables current-voltage spectroscopy at each pixel of the acquisition. The analysis of experimental datasets reveals localized electrical behavior of the material’s components as a function of applied voltage. Furthermore, machine learning algorithms are employed to predict current maps and cluster distinct regions based on their electrical characteristics.

关键词： Lithium-ion batteries Atomic measurements Atomic force microscopy Spectroscopy Microscopy Force Conductivity Nanoscale devices Cathodes Hyperspectral imaging

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Face Mask Detection in Real Time using Machine Learning algorithms

Face Mask Detection in Real Time using Machine Learning Algo...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Kavitha R Tanishq Sultania Padhmashri V Shreya A Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16’s deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time.

关键词： COVID-19 Training Face masks image segmentation Accuracy Computer viruses Face recognition Transfer learning Static VAr compensators Testing

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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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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

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German Microwave conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. The correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. The herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling Signal processing algorithms Radar Transforms Radar imaging Data models image restoration Frequency measurement Frequency division multiplexing

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