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

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Review on the image Encryption with Hyper-Chaotic systems 5th

Review on the Image Encryption with Hyper-Chaotic Systems

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5th International conference on Soft Computing and Signal processing, ICSCSP 2022

作者： Pathak, Arghya Pal, Subhashish Karmakar, Jayashree Mondal, Hrishikesh Mandal, Mrinal Kanti Department of Physics National Institute of Technology Durgapur713209 India Department of Physics Dr. B. C. Roy Engineering College Durgapur713206 India MUSE Lab Indian Institute of Technology Gandhinagar382355 India Department of Physics Durgapur Government College Durgapur713214 India

ISBN: (纸本)9789811986680

image encryption using a chaotic system is a widely used technique. But, cryptanalysis of most chaotic encryption algorithms pointed out the disadvantages or drawbacks of the chaotic systems. As a remedy, a new and more efficient chaotic system with higher dimension is used recently and named as a hyper-chaotic system. The conditions of an efficient encryption algorithm can be fulfilled effectively by a hyper-chaotic system. Recently, several hyper-chaotic systems are proposed by different researchers, and some of them are used in image encryption techniques. But, some recent cryptanalysis on hyper-chaotic image encryption shows the drawback of these system. This work will discuss different hyper-chaotic systems on the basis of their efficiency to use in image encryption. This discussion will also include the parameters based on which one can choose an efficient hyper-chaotic system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Chaotic systems

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Parameters Optimization of Dynamic CT Pulmonary Perfusion Imaging Analysis Technology Based on Artificial Intelligence

Parameters Optimization of Dynamic CT Pulmonary Perfusion Im...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Li, Chuangui Liu, Xiaocheng Zhang, Xinhui Zhou, Guijuan Zhu, Yuexiang First Affiliated Hospital of Hebei North University Department of Radiology Zhangjiakou China University of Chinese Medicine Dongzhimen Hospital of Beijing Department of Radiology Beijing China

ISBN: (纸本)9798350315455

In recent years, with the rapid development and popularization of artificial intelligence technology, CT has been more and more widely used in the field of clinical medicine as a non-volume micro body diagnostic method. In this paper, the artificial intelligence software is used to build a dynamic CTR lung scanning image based on the wavelet neural network model, and the genetic algorithm is used to improve the traditional visual lung image quality analysis model to improve the diagnostic efficiency. At the same time, the matrix representation weight value of the relationship between different types of disease maps is obtained by changing the threshold value to calculate the acoustic difference between the same CT tissue to determine its membership function and carry out joint optimization, After that, the effect of this technology was tested, and the results showed that the artificial intelligence based dynamic CT pulmonary perfusion imaging technology had faster generation time and higher quality than traditional algorithms in image processing, so as to achieve accurate mining of results and reduce the number of misdiagnosis. As a result, this method achieved 15 s for imaging test which comparatively lesser than Traditional imaging algorithms. © 2023 IEEE.

关键词： artificial intelligence dynamic CT lung image lung perfusion imaging parameter optimization

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A Generative Network with Dual-Domain Discriminators for Low-Dose Stationary Sources CT Imaging 6

A Generative Network with Dual-Domain Discriminators for Low...

引用

6th International conference on Digital Medicine and image processing, DMIP 2023

作者： Bai, Xiao Cheng, Ying Chen, Linjie Yang, Shuo Wang, Huamin Wang, Zhe Wu, Jiayi Cao, Guohua School of Biomedical Engineering ShanghaiTech University Shanghai China

ISBN: (纸本)9798400709425

Recent development of clinical Computed tomography (CT) technologies has led to research for novel CT systems that allow safer and faster imaging, such as low-dose cardiac CT imaging via stationary CT. However, the complex data acquisition schemes in stationary CT often cause severe artifacts and noise in the resulted images;this calls for the development of a new kind of image reconstruction algorithms. Recent advancements in deep learning have shown remarkable progress in medical image reconstruction, processing, and analysis. In this paper, we propose a generative network with dual-domain discriminators for low-dose CT reconstruction in a stationary CT system. The image-domain discriminator optimizes the generation network by comparing the generated CT images with the reference images, while the sinogram-domain discriminator preserves the structure of the sinograms and suppresses the noise. The network incorporates uncertainty to automatically adjust the weights of a multi-term loss function, eliminating the need for the manual tuning of hyperparameters in the loss function. The results from our numerical experiments demonstrate the effectiveness of our proposed reconstruction algorithm for low-dose imaging in stationary CT. © 2023 ACM.

关键词： image reconstruction

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Point spread function modeling for coherent plane-wave compounding ultrasound imaging 15

Point spread function modeling for coherent plane-wave compo...

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15th International conference on Information Optics and Photonics, CIOP 2024

作者： Zhou, Bowen Liu, Songde Tian, Chao School of Engineering Science University of Science and Technology of China Anhui Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Spatial resolution is a critical measure of the imaging performance of ultrasound imaging systems and is determined by various system factors. So far, no comprehensive studies have been reported to elucidate the impact of system factors on spatial resolution, which is generally defined as the full width at half maximum (FWHM) of the point spread function (PSF). This work aims to model the PSF of ultrasound imaging systems based on a linear transducer array and a coherent plane-wave compounding (CPWC) method and study how pulse-echo sequences, transducer parameters, and beamforming algorithms impact PSF. The Verasonics simulator and UltraSound ToolBox are used in numerical simulations for ultrasound signal generation and image reconstruction, respectively. Experiments are conducted to verify simulation results. Numerical and experimental results show that the value of axial resolution is negatively correlated with the center frequency of the ultrasonic transducer;the value of lateral resolution is positively correlated with the number of transmitted plane waves of the CPWC method and the element width of the ultrasonic transducer and is negatively correlated with the maximum steering angle of the CPWC method and the aperture size and center frequency of the ultrasonic transducer. Different beamforming algorithms show varying effects on spatial resolution and image quality. This work is expected to help predict and interpret the quality of ultrasound images produced by practical ultrasound imaging systems and is beneficial for the design and development of ultrasonic transducers, pulse-echo sequences, and beamforming algorithms for enhanced spatial resolution. © 2024 SPIE.

关键词： Optical transfer function

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Research on Polarization Imaging Target Detection Method Based on Multi-View 3

Research on Polarization Imaging Target Detection Method Bas...

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3rd International conference on Computer Graphics, image, and Virtualization, ICCGIV 2023

作者： Cui, Hao Wang, Xin Pu, Xiankun Shi, Lei Zhou, Zhiqiang Gao, Jun School of Computer and Information Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

ISBN: (纸本)9781510671720

Traditional multi-view target detection can only obtain the light intensity information of the target, and its detection ability is very limited in complex environments;Traditional polarization target detection mainly relies on a single camera to obtain the polarization information of the target, making it difficult to directly obtain all the polarization information of the target in some complex backgrounds. In order to overcome the limitations of the two detection methods mentioned above, this paper studies a multi angle polarization target detection method based on a polarization array system. This method mainly uses traditional multi perspective image processing algorithms to process polarized images and achieve polarization image stitching. To address the issue of the inability to directly concatenate polarized images in low light environments, this paper proposes the introduction of image enhancement algorithms to achieve the concatenation and target detection of multi angle polarized images in low light environments. Based on the characteristics of polarization feature images, a fusion method based on polarization features was studied to improve the contrast between the target and the background. © 2023 SPIE.

关键词： image enhancement

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Retinex by Autoencoders 16

Retinex by Autoencoders

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16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Pezzoni, Claudio Mio, Corrado Barsotti, Annalisa Univ Milan Dipartimento Informat Giovanni degli Antoni Via Celoria 18 I-20133 Milan Italy Khalifa Univ Sci & Technol EBTIC Abu Dhabi City U Arab Emirates Univ Milan Dipartimento Informat I-20133 Milan Italy

ISBN: (纸本)9781665464956

The Retinex algorithms find wide applications as image enhancers, for their capability of preserving edges, while at the same time attenuating smooth gradients and chromatic dominants. They are characterized by the fact that the output chromatic intensity of a pixel is not determined in isolation (or looking only at the contiguous pixels) but through an operation of comparison to different local and remote areas of the image. This local/global comparison implies also a high computational cost for the algorithms: their complexity is not linear with the number of pixels;furthermore, the more systematic the comparison, the higher the complexity. Thus, most Retinex algorithms are unfit for real-time processing. The recent development of efficient Machine Learning architectures for image processing has raised the question of whether one of the Retinex "transforms" could be efficiently learned by training a feed-forward Artificial Neural Network, thus creating a model characterized by short processing time. Selecting a variant of the Random Spray Retinex model - FuzzyRSR - as representative of the Retinex family, and choosing suitably structured autoencoder neural networks, we found that we could accurately reproduce the Retinex effects. The computational cost of the training phase was moderate, while that of the inference phases was linear in the number of pixels, and three orders of magnitude lower than the one of FuzzyRSR, thus making the ANN implementation of Retinex suitable for real-time processing.

关键词： Autoencoders FuzzyRSR image Enhancement Random Spray Retinex Retinex

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Utilizing image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series 38

Utilizing Image Transforms and Diffusion Models for Generati...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Naiman, Ilan Berman, Nimrod Pemper, Itai Arbiv, Idan Fadlon, Gal Azencot, Omri Department of Computer Science Ben-Gurion University of The Negev Israel

Lately, there has been a surge in interest surrounding generative modeling of time series data. Most existing approaches are designed either to process short sequences or to handle long-range sequences. This dichotomy can be attributed to gradient issues with recurrent networks, computational costs associated with transformers, and limited expressiveness of state space models. Towards a unified generative model for varying-length time series, we propose in this work to transform sequences into images. By employing invertible transforms such as the delay embedding and the short-time Fourier transform, we unlock three main advantages: i) We can exploit advanced diffusion vision models;ii) We can remarkably process short- and long-range inputs within the same framework;and iii) We can harness recent and established tools proposed in the time series to image literature. We validate the effectiveness of our method through a comprehensive evaluation across multiple tasks, including unconditional generation, interpolation, and extrapolation. We show that our approach achieves consistently state-of-the-art results against strong baselines. In the unconditional generation tasks, we show remarkable mean improvements of 58.17% over previous diffusion models in the short discriminative score and 132.61% in the (ultra-)long classification scores. Code is at https://***/azencot-group/imagenTime. © 2024 Neural information processing systems foundation. All rights reserved.

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GALOPA: Graph Transport Learning with Optimal Plan Alignment 37

GALOPA: Graph Transport Learning with Optimal Plan Alignment

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Wang, Yejiang Zhao, Yuhai Wang, Zhengkui Li, Ling Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Singapore Inst Technol InfoComm Technol Cluster Singapore Singapore

ISBN: (纸本)9781713899921

Self-supervised learning on graphs aims to learn graph representations in an unsupervised manner. While graph contrastive learning (GCL - relying on graph augmentation for creating perturbation views of anchor graphs and maximizing/minimizing similarity for positive/negative pairs) is a popular self-supervised method, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs to be achieved. In this work, we propose an alternative self-supervised solution that (i) goes beyond the label invariance assumption without distinguishing between positive/negative samples, (ii) can calibrate the encoder for preserving not only the structural information inside the graph, but the matching information between different graphs, (iii) learns isometric embeddings that preserve the distance between graphs, a by-product of our objective. Motivated by optimal transport theory, this scheme relies on an observation that the optimal transport plans between node representations at the output space, which measure the matching probability between two distributions, should be consistent with the plans between the corresponding graphs at the input space. The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance;(ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information;(iii) Our model maintains robust results even under high perturbation rates;(iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method.

关键词： Supervised learning

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image processing-Based Fire Detection and Protection System Using OPENCV

Image Processing-Based Fire Detection and Protection System ...

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2023 International conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Verma, Vivek Kr. Jain, Khushi Kumari, Jaya Kumar, Rohit Kumar, Umang ABES Engineering College Dept. of Electrical and Electronics Engineering Ghaziabad India

ISBN: (纸本)9798350329186

The objective of this project is to develop an advanced fire detection and protection system using image processing technology to provide early warning of fires. Traditional fire detection systems rely on smoke sensors that have limitations and are only capable of sensing fires in restricted areas. Our fire detection method based on a color model offers numerous advantages over traditional smoke detection methods. Its simplicity, feasibility, and comprehensibility make it a reliable and effective approach for detecting fires. To overcome these limitations, a Computer Vision Based Early Fire Detection system has been proposed which uses machine learning algorithms for enhanced accuracy. The system has been implemented using PyCharm IDE and either a primary or secondary camera. The input source is a webcam, which captures the video feed from the surrounding area for analysis. The code has been written in Python language using the OpenCV2 library for image processing. Then hardware is implemented which generates alerts to help prevent damage or injury. © 2023 IEEE.

关键词： Fires

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Enhanced Camouflage Detection Using Advanced image processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy image processing Surveillance image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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