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

共 16439 条记录，以下是1761-1770 订阅

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Recent advancements in image Steganography using Generative Adversarial Networks methods

Recent advancements in Image Steganography using Generative ...

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Syeda Imrana Fatima Yugandhar Garapati Department of Computer Science & Engineering GITAM (Deemed to be University) Hyderabad India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Generative Adversarial Networks are employed by GAN-based models in image steganography to efficiently conceal information from images while preserving their visual integrity. These models are made up of two primary parts: the discriminator which determines if an image is a real image or a stego-image, and the generator which embeds the hidden data into images. The generator learns how to embed data in a way that minimizes obvious distortions through adversarial training, where the discriminator and generator fight to make the hidden data less noticeable to detection algorithms and human observers. High data capacity and versatility are strengths of GAN-based steganography models, which enable efficient data concealment across a variety of image formats and steganographic techniques. They dynamically adjust the embedding procedure to strike a compromise between image quality and data capacity. They do, however, come with certain drawbacks such as the need for sophisticated model training, high processing overhead and hyperparameter sensitivity. Notwithstanding these drawbacks, GAN-based techniques mark a substantial breakthrough in the development of delicate and reliable image steganography techniques.

关键词： Training image quality Steganography Visualization Sensitivity Observers Generative adversarial networks Generators Data models Reliability

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Reversible Data Hiding in Encrypted images based on Classic McEliece Cryptosystem

Reversible Data Hiding in Encrypted Images based on Classic ...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Zongbao Jiang Minqing Zhang Fuqiang Di Weina Dong Key Laboratory of Network and Information Security of PAP Engineering University of PAP Xi’an China

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms’ embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm’s embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security.

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Bidirectional Iterative Diffusion Encryption Algorithm Based on Improved Chaotic systems

Bidirectional Iterative Diffusion Encryption Algorithm Based...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Juan Wang Boyong Gao College of Information Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Chaotic systems are widely used in cryptography due to their traversability, unpredictability and sensitivity to initial values. However, the traditional low-dimensional chaotic system has the disadvantages of small key space, short iteration period and easy to be constructed, which affects the security of encryption. Therefore, in order to solve this problem, this paper constructs a new one-dimensional chaotic mapping, which is experimentally shown to have wider chaotic range, better unpredictability and larger key space. On this basis, this paper proposes an encryption scheme that can encrypt different images of different sizes by iteratively diffusing the pixels in both directions through chaotic sequences, and the experimental results show the security and effectiveness of the algorithm.

关键词： Computer vision Sensitivity Correlation Chaotic mapping image processing Machine learning Color Iterative algorithms Encryption

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processing and Classification algorithms of Medical images

Processing and Classification Algorithms of Medical Images

引用

International image processing, Applications and systems conference (IPAS)

作者： Oltiana Seitaj Department of Industrial Engineering Electronics Mechanics University of Roma Tre Rome Italy

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional Neural Networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation.

关键词： Deep learning image segmentation Adaptation models Accuracy Thresholding (Imaging) Streaming media Reliability MATLAB Biomedical imaging Tumors

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UAV-Assisted Succulent Farmland Monitoring with Improved YOLOv10 and MambaIR

UAV-Assisted Succulent Farmland Monitoring with Improved YOL...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Hui Li Feng Xue Jiaqi Wang Dianhan xi Yongying Liu Mowen Zhang Guocheng Zhang Danhua Liu Chenyu Zhang Jianghan Tao Zhijie Zhang Yinyin He Qingyang Wu Yijia Chen Graduate School of Frontier Science The University of Tokyo Kashiwa Japan Dept. Math and Applied Mathematics China University of Petroleum-Beijing Karamay China Graduate School of Business and Finance Waseda University Tokyo Japan Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Graduate School of Global Environmental Studies Sophia University Tokyo Japan Dept. Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong China Dept. Environmental Health Sciences University of California Los Angeles US

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Succulent plant image categorization suffers from subjectivity and limited number of available images, and these difficulties are intensified by the apparent lack of field surveys. In this study, we present a new approach to classify succulents using consumer-grade Unmanned Aerial Vehicles (UAVs) and deep learning. We applied various super-resolution algorithms with different image enhancement design strategies. In addition, we develop a new object detection model based on MambaIR and YOLOv10, achieving a mean average precision (mAP) of 0.851, outperforming other state-of-the-art object detection algorithms. The application of UAV s and super-resolution technology greatly facilitates the detection of succulents and provides a practical solution for precision crop monitoring.

关键词： Deep learning Surveys Superresolution Object detection Reconstruction algorithms Autonomous aerial vehicles image categorization Classification algorithms Monitoring image enhancement

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Individual cow identification study based on DTW and K nearest Neighbor classification algorithm 2

Individual cow identification study based on DTW and K neare...

引用

2022 2nd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2022

作者： Su, Chenjie Cheng, xiaodong xi, Shiqi Li, Bomeng College of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (纸本)9783800760268

Individual cow identification based on computer vision using deep convolutional neural networks has a serious dependence on the amount of sample data, and there are limitations in collecting a large amount of image data under unfavorable factors such as light changes and pasture environment. This study proposes an individual cow identification method based on the DTW algorithm and K-nearest neighbor classification algorithm from the perspective of the uniqueness and Differences of the behavioral characteristics of dairy cows' activities. The cow activity data collected from the sensors worn on the cow's neck were used as input, and the similarity feature matrix between cows was generated based on the DTW algorithm after denoising with moving average and S-G filters respectively, which reduced the dimensionality of the data and decreased the number of data, and finally the similarity feature matrix was input to the K-nearest neighbor classification model for individual cow identification. On the experimental dataset, its recognition rate after S-G filter processing was 91.08%, and the scores of precision, recall, F1 score, and Mathews correlation coefficient were better than those after moving average processing. The results of this study indicate that this method can be effective for individual cow identification. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Nearest neighbor search

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Research on Algorithm of Laser Spot Center Positioning in Track Subsidence Monitoring System 5

Research on Algorithm of Laser Spot Center Positioning in Tr...

引用

5th International conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： xi, Zepeng Cao, Zhenfeng Yang, Zhe Yang, Minglai Shanghai Institute of Technology School of Rail Transportation Shanghai China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

In the track settlement monitoring system, the positioning accuracy of the spot center determines the accuracy of the monitoring system, and the common spot center positioning algorithms have certain shortcomings for the detection of the long-distance laser spot center. In this paper, the traditional Canny operator is improved, the high and low thresholds are determined by the OTSU algorithm, morphological processing is added to smooth the edge of the image, and finally the center of the light spot is located by the centroid method. The experimental results show that the method improves the stability and accuracy of the spot center positioning, and the achievement is successfully applied to the track settlement monitoring system. © 2022 IEEE.

关键词： Monitoring

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization 5

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

引用

5th International conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Vehicle Detection and Classification Based on image Recognition Techniques and Convolutional Neural Networks

Vehicle Detection and Classification Based on Image Recognit...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Danning Cao Yixuan Wang Chang'an University Xi'an China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In this study, a high-performance vehicle recognition and classification model is constructed based on convolutional neural network technology by using the image features of the license plate and Haar cascade detector to locate the location of the license plate, and it has achieved remarkable results in practical applications. The intersection and concurrency ratio of the model is as high as 0.9247, meanwhile, the recall rate reaches 0.9502, which proves that the model has a very high comprehensiveness and a low false alarm rate in detecting various types of vehicle instances. In addition, the Kappa coefficient reaches 0.9342, which further validates the consistency and accuracy advantage of the model on the classification task. This research result provides strong algorithmic support for real-time vehicle identification and accurate classification in intelligent transportation systems.

关键词： Training image recognition Accuracy Feature extraction Real-time systems Satellite images Convolutional neural networks Monitoring License plate recognition Testing

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image Registration for Virtual Drone Flight Simulation

Image Registration for Virtual Drone Flight Simulation

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2023 IEEE International conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Koniarski, Konrad Myśliński, Andrzej Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9798350327816

In the past decade, drones have expanded their applications, such as surveying, search and rescue, and last-mile delivery. Drone autonomy is a growing focus in these areas, requiring test environments for both drone operators and autonomous algorithms. This work addresses the creation of a realistic test environment for accurate position perception, motion planning, and drone control. Our approach uses RGB-D sensors and image registration techniques to construct a 3D map of the real environment, enabling drone navigation simulation. We adapt the Iterative Closest Point (ICP) algorithm, incorporating feature point filtering and local optical flow to expedite 3D point processing, particularly in static scenes. A challenge in the standard ICP algorithm is selecting suitable initial points for effective matching between consecutive point clouds. Our modified approach tackles this issue, optimizing the algorithm for 3D map construction. Since our application involves RGB-D image sequences with minimal variation between successive key frames, we use a scale-space pyramid and FAST techniques to select a limited number of key points, reducing the computational load. Moreover, by employing a local optical flow-based point-matching approach, we eliminate resource-intensive operations in each ICP optimization iteration. We demonstrate the practical utility of our method through a virtual 3D drone flight simulator. © 2023 IEEE.

关键词： drone operation simulation ICP registration structure from motion

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