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

共 12901 条记录，以下是421-430 订阅

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An Adaptive Reversible Data Hiding Scheme using image processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

引用

5th International conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. This study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). The goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security image processing Pixel Sorting Reversible Data Hiding Steganography

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Thresholding Techniques Comparsion on Grayscale image Segmentation 1st

Thresholding Techniques Comparsion on Grayscale Image Segmen...

引用

1st Analytics Global conference, AGC 2023

作者： Nassa, Vinay Kumar Wedpathak, Ganesh S. Department of Computer Science and Engineering Rajarambapu Institute of Technology Rajaramnagar India

ISBN: (纸本)9783031508141

image segmentation is a prime domain of computer vision backed by a huge amount of research involving both image processing-based algorithm and learning-based techniques. Due to this there is an upsurge in different segmentation technique from the research community. Various image segmentation techniques have their strength and weakness and some specific application are more geared up to some segmentation techniques. The automation systems like object detection, robotics and intelligent video analytics, do a lot of segmentation technique and hence there is need to evaluate the performance of these techniques. The paper implements the different types of segmentation techniques. Threshold techniques including like histogram thresholding, mean thresholding, edge thresholding, variable thresholding and percentile (P%-tile) exists. algorithms are applied using MATLAB coding on the considered images. White Pixel Ratio (WPR) parameter is used for analysis of various methods. Similar to the theoretical concept, practical approach shows that WPR is better for histogram thresholding as compared to other techniques. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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Lightweight Infrared and Visible image Fusion Based on Attention Mechanism and Receptive Field Enhancement

Lightweight Infrared and Visible Image Fusion Based on Atten...

引用

International conference on Communications, Signal processing, and systems, CSPS 2023

作者： Liu, Ting Zhang, Yuxin Fan, Yunsheng Luo, Peiqi Wang, Guofeng Marine Electrical Engineering College Dalian Maritime University Dalian116026 China

ISBN: (纸本)9789819975396

To balance the accuracy and speed of image fusion, a lightweight infrared and visible image fusion algorithm is proposed in this paper. Firstly, to improve the feature extraction capability, ECA attention is introduced to enhance information exchange among channels. Secondly, to improve the feature reconstruction ability and lightweight network, RFB is used to enlarge the receptive field and minimize parameters. Finally, experimental results show that the proposed method outperforms other algorithms in quality and speed. The quantitative results demonstrate that our method effectively preserves infrared features and visible details, with minimal color distortion and uniform brightness. Among them, params decreased by 59.5%, and time shortened by 4.38%. Therefore, the proposed method has high engineering application potential. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

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Energy performance profiling of a GPU-based CPM implementation 17

Energy performance profiling of a GPU-based CPM implementati...

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17th International conference on Signal-image Technology and Internet-Based systems, SITIS 2023

作者： Luca, Pasquale De Galletti, Ardelio Marcellino, Livia Parthenope University of Naples Science and Techonologies Department Naples Italy

ISBN: (纸本)9798350370911

In recent years, there has been a growing interest in the development of in vitro models to predict cellular behavior within living organisms. Mathematical models, based on differential equations and associated numerical algorithms, have been employed for this purpose. In this study, we present initial forays into the design of parallel strategies aimed at accelerating an algorithm for behavior prediction, specifically based on the Cellular Potts Model. To do this, we engage the computational power of Graphic processing Units within the CUDA environment to optimize critical low-level *** work intends to provide a comprehensive analysis of the energy performance of the proposed implementation. Tests and experiments affirm significant performance gains in terms of both processing time and substantial energy savings. © 2023 IEEE.

关键词： Graphics processing unit

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OpenCV Implementation of image processing Optimization Architecture of Deep Learning Algorithm based on Big Data processing Technology

OpenCV Implementation of Image Processing Optimization Archi...

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2022 International conference on Sustainable Computing and Data Communication systems, ICSCDS 2022

作者： Wang, Weijun Wang, Junquan Zhang, Zhiqiang Shi, Da Chengdu University School of Computer Chengdu Sichuan 610106 China Sichuan Silver-valley Science and Technology Development Co. Ltd. Chengdu Sichuan 610000 China

ISBN: (纸本)9781665478847

Through the in-depth analysis of artificial neural network algorithm technology, particle swarm algorithm technology, and image matching algorithm, the article briefly analyzes the theoretical principle of the algorithm, analyzes the characteristics of the algorithm, and analyzes the application of the algorithm in image processing optimization technology. Using the iterative process of neurons to process image data, after dimensionality reduction, reduction, and white point removal, the optimization of image processing is increased by 7.5%. Using deep learning algorithms to optimize the processing of the drawbacks and difficulties of big data, build an image optimization architecture framework and OpenCV modeling, the results showthat image noise is reduced by 12%. © 2022 IEEE.

关键词： Big data

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Proceedings of the 2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

Proceedings of the 2024 International Conference on Innovati...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

ISBN: (纸本)9798331543617

The proceedings contain 396 papers. The topics discussed include: development of an IoT enabled smart children tracking and monitoring system using map location services;HMLM: an intelligent artificial intelligence assisted strategy to identify UPI frauds based on hybrid Markov learning methodology;data-driven insights into automotive customer complaints: a machine learning approach to predictive analytics;integrating predictive analytics and deep learning for vehicle safety incident forecasting;analyzing the health status of people in rural areas using machine learning algorithms;machine learning based approach using hand gestures for mouse and video control;experimental analysis of artificial intelligence powered adaptive learning methodology using enhanced deep learning principle;a comprehensive analysis of image processing methods for agricultural product quality control;and convolutional neural network-based multi-fruit classification and quality grading with a Gradio interface.

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Skin Cancer Detection Using Deep Learning Approach 7th

Skin Cancer Detection Using Deep Learning Approach

引用

7th International conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ankush Singh, Samridhi Singh, Nagendra Pratap Rathee, Priyanka Department of Computer Science and Engineering National Institute of Technology HP Hamirpur India

ISBN: (纸本)9789819908370

Skin cancer is a severe health issue. Thus, the major concern of physicians is to investigate a precise clinical diagnosis. At present, some mechanisms are developed in the area of image processing with the help of algorithms and systems to detect and classify skin cancer. Computer-based technology provides a relaxed, inexpensive and quick diagnosis of skin cancer symptoms. Several techniques, non-invasive in nature, have been proposed to investigate the symptoms of skin cancer, whether they represent melanoma or non-melanoma. The general process applied in skin cancer detection is image acquisition, pre-processing, segmentation of the acquired pre-processed image, extraction of the required features and classification of disease skin cancer. After implementing the model we get an accuracy of 88.48% in the case of AlexNet and 90.41% in the case of VGG 16 respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image acquisition

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Efficient Equivariant Transfer Learning from Pretrained Models 37

Efficient Equivariant Transfer Learning from Pretrained Mode...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Basu, Sourya Katdare, Pulkit Sattigeri, Prasanna Chenthamarakshan, Vijil Driggs-Campbell, Katherine Das, Payel Varshney, Lav R. Univ Illinois Champaign IL 61820 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781713899921

Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba et al. (2022) propose group averaging (equitune) and optimization-based methods, respectively, over features from group-transformed inputs to obtain equivariant outputs from non-equivariant neural networks. While Kaba et al. (2022) are only concerned with training from scratch, we find that equitune performs poorly on equivariant zero-shot tasks despite good finetuning results. We hypothesize that this is because pretrained models provide better quality features for certain transformations than others and simply averaging them is deleterious. Hence, we propose lambda-equitune that averages the features using importance weights, lambda s. These weights are learned directly from the data using a small neural network, leading to excellent zero-shot and finetuned results that outperform equitune. Further, we prove that lambda-equitune is equivariant and a universal approximator of equivariant functions. Additionally, we show that the method of Kaba et al. (2022) used with appropriate loss functions, which we call equizero, also gives excellent zero-shot and finetuned performance. Both equitune and equizero are special cases of lambda-equitune. To show the simplicity and generality of our method, we validate on a wide range of diverse applications and models such as 1) image classification using CLIP, 2) deep Q-learning, 3) fairness in natural language generation (NLG), 4) compositional generalization in languages, and 5) image classification using pretrained CNNs such as Resnet and Alexnet.

关键词： image classification

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Multi-stage image Deraining based on Pre-trained Diffusion Model 6

Multi-stage Image Deraining based on Pre-trained Diffusion M...

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6th ACM International conference on Multimedia in Asia, MMAsia 2024

作者： Zeng, Xiong Jiang, Min Huang, RongHua School of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798400712739

image deraining typically involves synthesizing low-quality degraded data for training using a predefined degraded model of a single weather condition. While in real world scenarios, varying rain intensities result in different sizes and densities of raindrops and rain streaks, increasing the complexity of image degradation. In this paper, we proposed a multi-stage deraining framework based on pre-trained diffusion model, it can efficiently perform the rain removal task under a variety of weather situations. We diffuse degraded images into a noisy state where various types of degradation are transformed into Gaussian noise. Then, during the denoising process, the low-frequency information of the image is replaced through iterative refinement, guiding the pre-trained diffusion model for image reconstruction. Our method effectively utilizes the generative priors in diffusion models and avoid the computational burden of retraining conditional diffusion models. Experimental results on four rainy degradation image datasets show its robustness to different types and severities of degradation (such as raindrops and rain streaks). Compared to recent deraining algorithms, our method achieves a maximum improvement of 0.96 dB (3.5%) in PSNR and 0.021 dB (2.7%) in SSIM for the restored images. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Rain

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AI Mediated Surface Waste Auto-Detection on Water Bodies Using Cutting Edge Technology

AI Mediated Surface Waste Auto-Detection on Water Bodies Usi...

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2024 IEEE International conference on Cognitive Computing and Complex Data

作者： Choudhary, Vijaya Guha, Paramita Archana Dey, Sharmistha IILM Univ Sch Comp Sci & Engn Plot 16-18Knowledge Pk 2 Greater Noida Uttar Pradesh India Acad Sci & Innovat Res AcSIR CSIR Natl Phys Lab Dr KS Kushnan Marg New Delhi India OP Jindal Global Univ Sonipat India Galgotias Univ Sch Comp Sci & Applicat Greater Noida India

ISBN: (纸本)9798350352900;9798350352894

The proliferation of surface waste in water bodies poses significant environmental and ecological challenges. Traditional methods of waste detection are often labor-intensive and limited in scope. This paper presents a novel approach to surface waste detection using artificial intelligence (AI) and advanced imaging technologies. Leveraging cutting-edge techniques such as deep learning algorithms, high-resolution satellite imagery, and real-time data processing, our system offers an automated solution for identifying and monitoring waste in water bodies. We developed a robust AI model trained on diverse datasets, including satellite and drone-captured images, to detect various types of surface waste with high accuracy. The system integrates real-time processing capabilities to provide timely alerts and actionable insights for environmental management. Evaluation results demonstrate that our approach significantly improves detection accuracy and operational efficiency compared to conventional methods. This research contributes to the advancement of smart environmental monitoring systems and offers a scalable solution for mitigating the impact of surface waste on aquatic ecosystems.

关键词： Artificial Intelligence Garbage detections Convolutional neural network river research image processing computer vision

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