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

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Interpreting and understanding the image using deep learning and imprecise information from the monitoring system

Interpreting and understanding the image using deep learning...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Kubanek, Mariusz Bobulski, Janusz Karbowiak, Lukasz Czestochowa University of Technology Department of Computer Science Czestochowa Poland

ISBN: (纸本)9798350324457

The interpretation and understanding of images using deep learning algorithms in monitoring systems is an important area of research in computer vision. In this paper, we propose a novel approach for interpreting and understanding images of human behaviour captured from cameras and drones in monitoring systems. In the work, we presented a pedestrian detection method. This is the first element of a larger system of interpreting the behaviour of traffic participants. We begin by pre-processing the images to remove noise and unwanted artefacts. We then use labelled and unlabelled data to train a deep learning algorithm to accurately classify and predict human behaviour. To account for imprecise information, we incorporate probabilistic models into the algorithm to allow it to make predictions with uncertainty. To evaluate the performance of our proposed approach, we use standard metrics, such as precision, recall, and F1 score, and compare our results to existing methods. We also identify the limitations of existing approaches and propose new methods for interpreting and understanding images that can improve their accuracy and reliability. Our proposed approach has the potential to provide a more accurate and robust method for interpreting and understanding images of human behaviour in monitoring systems. It can be used in various applications like traffic monitoring, security systems, and surveillance. Our results demonstrate the importance of incorporating imprecise information into deep learning algorithms to improve their performance and highlight the potential for future research in this area. © 2023 IEEE.

关键词： autonomous car computer vision pedestrian safety smart city

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An Investigation on Classical Methods and Unsupervised Deep Learning algorithms for image Segmentation in High Variance images for Industrial Application

An Investigation on Classical Methods and Unsupervised Deep ...

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Irish Signals and systems conference (ISSC)

作者： Nathan Henry Tamás Reiter

ISBN: (数字)9798350352986

ISBN: (纸本)9798350352993

This paper investigates classical image processing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns generated by Focused Ion Beam (FIB) technology. Classical methods (CM) include preprocessing techniques such as edge detection, while Deep Learning (DL) employs Convolutional Neural Networks (CNNs) for unsupervised image segmentation. Experimental results demonstrate the superior accuracy of DL, but with increased computational overhead, in contrast to the efficiency and controllability of CM. Combining both approaches offers a potential solution for optimizing accuracy and efficiency in image segmentation.

关键词： Deep learning image segmentation Accuracy image edge detection Current measurement Noise reduction Noise

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Feature Decoupled Autoencoder: Semi-Supervised Learning for image Dehazing

Feature Decoupled Autoencoder: Semi-Supervised Learning for ...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Wang, Jindong Wang, Fan Yin, Dong School of Information Science and Technology University of Science and Technology of China China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Single image dehazing plays an important role in image processing. Now, most image dehazing methods only use synthetic datasets to train models which produce some poor results on the real hazy images. To solve this problem, we propose a new network named feature decoupled autoencoder which can be trained by semi-supervised learning due to the real hazy images lacking labels. Our autoencoder includes a feature decoupled encoder and a feature fusion decoder. The clear image feature and the hazy feature are decomposed by the encoder and merged by the decoder. When the hazy feature is set to 0, the decoder will produce a dehazing image relying on the clear image feature. By training on the synthetic datasets and real datasets together, our model has good dehazing ability on the real-world hazy images. On the HazeRD and SOTS, our model's performance is comparable to the state-of-the-art algorithms. © 2022 IEEE.

关键词： Signal encoding

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Efficient implementation of codebook algorithm for background subtraction

Efficient implementation of codebook algorithm for backgroun...

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Embedded systems and Artificial Intelligence (ESAI), International conference on

作者： Tebbaa Brahim Abdessamad Klilou Smail Bariko Assia Arsalane Microelectronic Embedded Systems and Telecommunications (MiSET) Team Faculty of Sciences and Technologies Sultan Moulay Slimane University Beni Mellal Morocco Automation and Energy Conversion (EACE) Team Faculty of Sciences and Technologies Sultan Moulay Slimane University Beni Mellal Morocco Laboratory of Engineering and Applied Technologies Higher School of Technology Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

Background subtraction is a critical component of numerous image processing applications, particularly in video surveillance. Considerable effort has been devoted to enhancing the accuracy of codebook algorithms. However, these improvements have led to significant increases in computing power requirements, rendering them impractical for real-time applications. The performances of codebook algorithms are mainly due to its inherent complexity and the variable number of codewords dedicated to represents each pixel. Which requires frequent memory allocation and deallocation. To address these limitations, we propose optimizing the codebook model in order to reduce its size while preserving accuracy and enabling real-time processing.

关键词： Accuracy Embedded systems image processing Computational modeling Memory management Video surveillance Rendering (computer graphics) Real-time systems Complexity theory Resource management

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Low-illumination image Enhancement Method Based on Adaptive MSRCR Algorithm 7

Low-illumination Image Enhancement Method Based on Adaptive ...

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7th International conference on Computational Linguistics and Intelligent systems. Volume I: Machine Learning Workshop, CoLInS 2023

作者： Ning, Xiongwei Su, Jun Kochan, Orest Hubei University of Technology No.28 Street NanLi Wuhan430068 China Lviv Polytechnic National University 12 S. Bandera Str. Lviv Ukraine

The low-illumination image enhancement method based on the adaptive MSRCR algorithm is proposed to address the problems of the Retinex algorithm in processing low-illumination images, such as the need to manually adjust parameters and blurred details. In the HSV color space of the original image, the luminance V component is decomposed by mean filtering to create a detail layer, and the detail layer information is enhanced by using enhancement weights. The improved Salp Swarm Algorithm (LLSSA) is proposed for adaptive parameter adjustment of Multi-Scale Retinex with Colour Recovery (MSRCR) and detail layer weights, which uses Logistic Chaos to initialise the salps population and introduces Lévy flights into the updated positions of leaders and followers to enhance the global search capability. Finally, the adaptive MSRCR enhancement map and the detail layer enhancement map are images fused to produce a final enhanced image with clear details. The experimental results show that compared with several typical algorithms, the algorithm in this paper can effectively maintain the image details, improve the image brightness and have better visual effects. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： image enhancement

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Color image segmentation based on thresholding for advanced driver assistance systems

Color image segmentation based on thresholding for advanced ...

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IEEE Zooming Innovation in Consumer Technologies conference (ZINC)

作者： Budak, Luka Grbic, Ratko Cetic, Nenad Kastelan, Ivan TTTech Auto CEE Cara Hadrijana 10b Osijek Croatia Fac Elect Engn Comp Sci & Informat Technol Kneza Trpimira 2b Osijek Croatia Res & Dev Inst RT RK Narodnog Fronta 23A Novi Sad Serbia Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad Serbia

ISBN: (纸本)9781665483759;9781665483742

Many Advanced Driver Assistance systems (ADASs) rely on an image obtained by a camera that is mounted on a vehicle. To get useful information in real-time, the acquired image is processed with different computer vision algorithms running on the vehicle's embedded platform. The common preprocessing task is the image segmentation based on color which is often used in lane detection or traffic light/sign recognition algorithms to extract key regions. In this paper, we focus on a color image segmentation based on thresholding. While being very simple, its effectiveness largely depends on the details of the implementation such as the chosen color space or characteristics of the used embedded platform. We provide details regarding PC implementation and ADAS development board implementation as well as the details regarding optimizations that are carried out to achieve smaller execution time on PC and board's Texas Instruments TDA2xx System-On-Chip. The image segmentation mean processing time is reported for three different resolutions and three different color models (RGB, HSV, YUV) for both PC and ADAS development board. The obtained results can help in planning and allocating resources on the vehicle's embedded platform for such computer vision tasks.

关键词： ADAS image segmentation image thresholding VisionSDK TDA2xx

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image Classification With The Hybrid PVCN 9

Image Classification With The Hybrid PVCN

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9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Pranaya, B. Kumar, A. Vinay Pruthviraj, C. Gangothri, N. Chatterjee, Priyadarshini B v Raju Institute of Technology Computer Science and Engineering Telangana Gajwel India B v Raju Institute of Technology Computer Science and Engineering Telangana Kodad India B v Raju Institute of Technology Computer Science and Engineering Telangana Sangareddy India B v Raju Institute of Technology Computer Science and Engineering Telangana Medak India B v Raju Institute of Technology Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350384208

Our research introduces an innovative methodology that seamlessly integrates ResNet-50, an advanced Convolutional Neural Network (CNN), with the HalfUNet architecture, enriched by the attention-gate mechanism, to address the complex task of classifying color and black-and-white images with exceptional precision. Our primary objective is to achieve highly accurate classification while effectively discerning between color and black-and-white images. At the core of our approach lies meticulous data preprocessing, incorporating advanced denoising techniques that substantially enhance the quality of our dataset, yielding an impressive Peak Signal-to-Noise Ratio (PSNR) of 71.35%. This PSNR score substantiates the superior quality of our refined dataset and ensures the consistent performance of our model. Leveraging state-of-the-art deep learning techniques and harnessing the unique capabilities of the Half-UNet architecture, particularly its image classification process, enhanced by the attention-gate mechanism, our approach attains an outstanding image classification accuracy rate of 91.3%. This remarkable achievement underscores the robustness and adaptability of our model, enabling it to perform effectively even when confronted with unfamiliar colorization algorithms during testing and limited prior training data. Our research offers an innovative solution that combines ResNet-50 with Half-UNet, incorporating with 1000 images of rigorous dataset pre-processing, quality assessment, and the attention-gate mechanism to achieve remarkable accuracy in image categorization and colorization tasks. © 2023 IEEE.

关键词： image classification

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Generalizable Real-time Accelerated Dynamic MRI

Generalizable Real-time Accelerated Dynamic MRI

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

作者： Silpa Babu Wahidul Alam Rushdi Zahid Rusho Sajan Goud Lingala Namrata Vaswani ECE Dept Iowa State University USA BME Dept University of Iowa USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce a real-time undersampled dynamic MRI algorithm, termed FewShot-AltGDmin-MRI, that is generalizable: works for many different applications and sampling trajectories without any application-specific parameter tuning. FS-AGM-MRI operates in real-time after processing the first short mini-batch, i.e., it can provide a reconstruction of each new image frame as soon as the MRI scan data for that frame arrives. It also provides a second set of improved quality reconstructions after a short delay. We compare our algorithm against many state of the art batch MRI algorithms, including Deep Learning (DL) based ones, on 17 different retrospectively undersampled datasets and two prospective datasets. FS-AGM-MRI is the only approach that provides accurate recovery for all datasets while also being one of the fastest.

关键词： Magnetic resonance imaging Heuristic algorithms Signal processing algorithms Signal processing Real-time systems Trajectory Delays Speech processing image reconstruction Tuning

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Enhancing Blurred image processing with IoT Integration for Improved Clarity

Enhancing Blurred Image Processing with IoT Integration for ...

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Emerging Research in Computational Science (ICERCS), International conference on

作者： Geetamma Tummalapalli Summia Parveen H Ramesh S M Monica P Santhiya B Arun Sekar Rajasekaran Department of Electronics and Communication Engineering GMR Institute of Technology Rajam India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering GST GITAM Deemed to be University Visakhapatnam India Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering SR University Warangal India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains.

关键词： image sensors Cloud computing Privacy image processing image edge detection Noise Visual effects Real-time systems Internet of Things Object recognition

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Crops Classification Using Machine Learning And Google Earth Engine 14

Crops Classification Using Machine Learning And Google Earth...

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14th International conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Achahboun, Chakir Chikhaoui, Mohamed Naimi, Mustapha Bellafkih, Mostafa Iav Hassan 2 Ressources Naturelles et Environnement Rabat Morocco Institut National des Postes et Télécommunications Rabat Morocco

ISBN: (纸本)9798350308211

The primary objective of this study was to address the critical challenge of obtaining accurate information regarding the spatial distribution and classification of crops in agricultural areas. The aim was to enhance agricultural decision-making and management, especially in regions with limited water resources, where crop productivity and sustainability are crucial for achieving food security and sustainable development. To achieve this objective, this study utilized machine-learning classification algorithms in conjunction with Landsat and Sentinel satellite imagery. The classification of different crops was based on Normalized Difference Vegetation Index (NDVI) phenology. The classification process and post-processing were conducted using the Google Earth Engine (GEE) platform , as well as utilizing Python, and scikit-learn library. Ground-truth data provided by local experts, along with the EUROMAP 2018 dataset in South Spain, were used to label the classification results. The findings of this study demonstrated a classification accuracy of 72% for certain crop types, indicating significant implications for sustainable agricultural practices and land use planning. © 2023 IEEE.

关键词： image classification

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