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

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Learning systems in Forgery image Detection 1st

Learning Systems in Forgery Image Detection

引用

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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A Novel image Segmentation Method Based on Random Walk Combined with Robust Sampling Prior

A Novel Image Segmentation Method Based on Random Walk Combi...

引用

International conference on Business Intelligence and Information Technology, BIIT 2023

作者： Yao, Guilin Li, Huimin Yang, Huixin Li, Shizhou Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China

ISBN: (纸本)9789819739790

In the image segmentation technique, how to accurately solve the boundary of foreground objects and background is the key problem, and the typical unsupervised interactive binary segmentation algorithms in this include Grab Cut, random walker and K-means clustering, etc. Among them, the random walker algorithm is a classical algorithm of segmentation based on graph theory, which has the advantages of strong interpretation and high efficiency and is suitable for segmentation fields with low boundary requirements. However, because the random walk algorithm only uses the neighborhood information of unknown pixels and does not make full use of the sample prior information at longer distances, the results of this method are segmented more coarsely. This paper proposes a random walker image segmentation method based on robust sampling. The method selects the sample points that are far from the unknown pixels for robust sampling and selects the best foreground and background sample pairs for the calculation of the foreground prior probability. After that, the computed prior probability values are fused with the random walker algorithm as a way to improve the accuracy of the segmentation results. The experimental results show that the algorithm of the addition of prior information can obtain better segmentation results than the original random walker segmentation. The experiments in this paper compare the mean square error values of some typical images to further validate the effectiveness of this method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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Faster image Deblurring for Unmanned Aerial Vehicles

Faster Image Deblurring for Unmanned Aerial Vehicles

引用

International conference on Unmanned Vehicle systems-Oman (UVS)

作者： Viktor Sineglazov Kyrylo Lesohorskyi Olena Chumachenko dept of Aeronavigation Electronics and Telecommunication National Aviation University Kyiv Ukraine dept of Artificial Intelligence IASA National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

This work is devoted to the development of a novel deep learning encoder-decoder algorithm for real-time noise and blur elimination in video frames, received from UAV. This work improves on existing algorithms by providing a more flexible blind deblurring solution than existing kernel-based methods. The proposed method can be applied to both improve the drone operator's capabilities and to improve the performance of autonomous image processing tasks, such as object identification and visual navigation systems. Different types of blur as well as possible types of noise are presented. A brief overview of existing methods is provided. The problem of frame alignment due to the object's movement and associated noise is considered. Existing deblurring and image restoration methods are reviewed, including state-of-the-art. Their limitations are highlighted. To solve the limitations a method based on a fully convolutional encoder-decoder network with residual connections is presented. Dataset generation and training procedures are discussed. The approach is then compared to existing state-of-the-art deep learning methods. The proposed method enables up to 9 times faster blind image restoration with comparable quality in comparison to existing state-of-the-art image restoration methods.

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image to Audio Conversion to Aid Visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

引用

4th International conference on Electronics and Sustainable Communication systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 IEEE.

关键词： Convolutional neural networks

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Real-Time Pedestrian Detection in Hazy Weather for Surveillance systems

Real-Time Pedestrian Detection in Hazy Weather for Surveilla...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Bouafia Yassine Guezouli Larbi Hicham Lakhlef Computer Science Department LaSTIC Laboratory University of Batna2 Algeria Batna Algeria Higher National School of Renewable Energy Environment and Sustainable Development LEREESI Laboratory Batna Algeria Sorbonne University University of Technology of Compiègne Compiègne France

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Real-time pedestrian detection is an expanding research topic that is crucial in various vision-based applications. Effective detection of pedestrians in various environments and situations will significantly improve human safety. However, the performance of current pedestrian detection algorithms faces challenges in scenarios with reduced visibility and blurred appearance, such as hazy weather conditions. This study addresses this issue by seeking an optimal balance between accuracy and speed for deploying deep learning-based pedestrian detection in real-time intelligent surveillance systems. For this aim, we used the transfer learning technique to fine-tune small versions of state-of-the-art YOLO models (YOLONAS-small, YOLOv8-small, YOLOv6-small, and YOLOv5-small) to perform pedestrian detection in hazy weather effectively. Experiment results demonstrate the effectiveness of our methods in accurately detecting pedestrians under hazy conditions by achieving high average precision and F-Score. Moreover, they all operate in real time, with impressive processing speeds ranging from 52.41 to 186.22 FPS.

关键词： YOLO Training Pedestrians Surveillance Transfer learning Signal processing Real-time systems

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Digital Modulation Identification in Spatial Modulation MIMO systems

Digital Modulation Identification in Spatial Modulation MIMO...

引用

International conference on image and Signal processing and their Applications (ISPA)

作者： Dalil Beknadj Hakim Tayakout Mohamed Tounsi Fatma Zohra Bouchibane Elhocine Boutellaa LIMed Laboratory University of Bejaia Bejaia Algeria Center for Development of Advanced Technologies (CDTA) Algiers Algeria LAMOS Laboratory University of Bejaia Bejaia Algeria Institute of Electrical Engineering and Electronics University Mhamed Bougara Boumerdes Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Cognitive radio (CR) is a game-changing technology that allows devices to adjust transmission and/or reception parameters in real time, improving wireless communication efficiency. Digital modulation identification (DMI) plays a crucial role in in CR, wireless communications, and spectrum management, providing adaptive and efficient signal processing. In this paper, we introduce a DMI approach designed for spatial modulation (SM) within the context of wireless multiple input multiple output (MIMO) technologies. In this study, we use the higher order statistics (HOS) as feature-based digital modulation identification (FB-DMI) to assess the performance of frequently utilized classifiers namely, k-nearest neighbor (KNN), and decision tree (DT). To train these classifiers, several configurations of the SM-MIMO network were evaluated, using the zero forcing (ZF) equalizer. Experiments show that KNN outperforms DT in identifying modulation schemes across all SNR levels.

关键词： Resistance Digital modulation Signal processing algorithms Interference Robustness Real-time systems Object recognition

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Maritime image Stabilization: A Comprehensive Review of Techniques and Challenges

Maritime Image Stabilization: A Comprehensive Review of Tech...

引用

Electronic Technology and Information Science (ICETIS), International conference on

作者： Enping Wei Yong Chai Tan Vin Cent Tai Yanan Hao Xiaodong Zhang Tian Zhang Centre for Modelling and Simulation Faculty of Engineering Built Environment and Information Technology SEGI University Kuala Lumpur Malaysia

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust image processing algorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.

关键词： Vibrations Visualization Reviews Target recognition Machine vision Stability criteria Object detection

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Efficient Person Detection on Single Board Computers: A Comparative Analysis of algorithms

Efficient Person Detection on Single Board Computers: A Comp...

引用

International conference on Advanced Computing and Communication systems (ICACCS)

作者： Shrajan Jain Abhishek Kumar Pooja Goswami Raghav Khajuria Prateek Dayal Somnath Banerjee CRL – Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

This research paper conducts an exhaustive technical evaluation and comparative analysis of person detection algorithms optimized explicitly for deployment on single-board computers (SBCs). The burgeoning demand for edge computing solutions, particularly on constrained platforms like SBCs, necessitates the optimization of object detection algorithms, specifically targeting person detection. This study meticulously examines and contrasts the performance of varied person detection algorithms tailored to SBCs, meticulously scrutinizing metrics encompassing accuracy, computational efficiency, CPU utilization, and latency. By conducting rigorous experimentation and in-depth analysis, this research dissects the intricate trade-offs inherent in algorithmic intricacy, computational efficacy, and the real-time inference capabilities crucial for edge computing scenarios. The research outcomes yield profound technical insights essential for strategic algorithm selection, aiding in the identification of the most adept person detection algorithm primed for deployment on SBCs across diverse applications, spanning surveillance systems to edge-driven robotics. Ultimately, this comprehensive comparative analysis serves as a robust technical blueprint, offering vital guidance for refining person detection algorithms on resource-constrained edge devices, thereby propelling advancements in edge computing and bolstering real-time object detection capabilities.

关键词： Measurement YOLO Accuracy Computational modeling image edge detection Real-time systems Inference algorithms Central processing Unit Detection algorithms Edge computing

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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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Surface Drift Debris Segmentation and Visualization through YCbCr Colour Space Thresholding using Low Power FPGA

Surface Drift Debris Segmentation and Visualization through ...

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International Symposium on VLSI Design and Test (VDAT)

作者： Sarath Kumar K Akash Iyer Swastik Raj Behera K Sivasankaran School of Electronics Engineering VIT University Vellore India

ISBN: (数字)9798350380101

ISBN: (纸本)9798350380118

The accumulation of oceanic debris is a matter of increasing concern in today’s world. Thus, it is crucial to develop an autonomous solution that can segment debris in real-time and at high speeds while maintaining cost-effectiveness, low power consumption, and scalability. FPGAs are well-known to be low-power, high-speed alternatives to CPUs and GPUs that can also be reprogrammed at the user’s convenience, allowing for improvements to be made to the system easily. However, segmentation algorithms typically utilize complex machine learning or image processing algorithms that are difficult to implement effectively on hardware. In this work, an algorithm using thresholding in the YCbCr colour space is proposed and its accuracy is evaluated using Entropy, True Positive Rate, and True Negative Rate. The segmented regions in all the test images are found to have an entropy greater than 7 while the average True Positive Rate is 91.14%. The average True Negative Rate of the segmented regions is found to be 93.8%. The algorithm was deployed on a Cyclone iv-E FPGA wherein live camera feed was processed on the FPGA and the output was visualized on a VGA monitor. The overall performance was satisfactory, although some glitches and jitters were observed, likely due to post-processing steps required for integrating the camera feed with the VGA monitor. The algorithm proved to be robust in segmenting ocean debris under good lighting conditions. However, accuracy decreased when the ocean colour significantly deviated from its expected blue or when the image was not an aerial view.

关键词： image segmentation Visualization Thresholding (Imaging) Machine learning algorithms Accuracy image color analysis Real-time systems Entropy Feeds Field programmable gate arrays

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