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

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3D image Reconstruction and processing for Augmented and Virtual Reality Applications: A Computer Generated Environment 2

3D Image Reconstruction and Processing for Augmented and Vir...

引用

2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Baskar, Radhika Krishnammal, P. Muthu Aeron, Anurag Ali, Syed Sumera Leonid, T. Thomas Arun, M.R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computing Dit University Computer Science and Engineering Dehradun India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Ece Dept Kcg College of Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350369960

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, 'texture mapping' enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people. © 2023 IEEE.

关键词： 3D image Augmented reality Reconstruction Virtual Reality

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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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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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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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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

引用

17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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Data Optimization Method for Ship Infrared image Recognition

Data Optimization Method for Ship Infrared Image Recognition

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2023 International conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhang, Haoxiang Liu, Chao Ma, Jianguan Sun, Hui University of Electronic Science and Technology of China School of Mathematical Sciences Chengdu China Beijing China

ISBN: (纸本)9798350312492

This study aims to address two challenging problems that affect the accurate and reliable recognition of ship infrared (IR) images in various scenarios: the interference of radiation highlights from specular reflections of target background sunlight and the difficulty of distinguishing ship targets from background confusion under low light conditions. We propose a data optimisation method for ship IR deep learning recognition, which uses processing tools such as median noise reduction and local histogram equalisation image enhancement to improve the performance and stability of ship IR recognition under the above unfavourable conditions. And the effectiveness of the method is verified on high fidelity mid-wave infrared (MWIR) ship simulation data using deep learning image classification algorithms such as AlexNet, VggNet and EfficientNet. To further demonstrate the effectiveness of the method, research has also adopted model feature gradient visualization technique, which indirectly verifies that our method can effectively improve the predictive reliability of ship recognition models. Overall, the data optimization method proposed in this study provides a feasible solution to address the challenges in ship IR image recognition and can provide a valuable reference for military mission-related research. © 2023 IEEE.

关键词： Infrared radiation

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

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

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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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image Registration for Virtual Drone Flight Simulation

Image Registration for Virtual Drone Flight Simulation

引用

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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Automatic Segmentation of Rectal Tumor on Magnetic Resonance images via a Deep Discriminative Model Consisting of Asymmetric U-Net and Convolutional Conditional Random Field based Post-processing 19

Automatic Segmentation of Rectal Tumor on Magnetic Resonance...

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19th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Li, Xing Chen, Hao Ge, Sisi Xi'an University of Posts and Telecommunications School of Computer Science Xi'an710121 China Xi'an University of Posts and Telecommunications School of Humanities and Foreign Languages Xi'an710121 China

ISBN: (纸本)9798350304398

The process of manual delineating is frequently time-consuming and can result in low consistency. Our goal was to design a deep discriminative model (DDM) to mitigate these issues of magnetic resonance imaging (MRI) for rectal tumor lesion boundary delineation, and we proposed an asymmetric U-Net rectal tumor MR images segmentation algorithm based on multi-scale dilated convolutional inputs and convolutional conditional random fields (ConvCRFs). A multiscale convolutional input module was designed as a preprocessing step to enrich the extraction and input of global contextual semantic information;then, an asymmetric U-Net network combined with ConvCRFs was used to discriminatively fine-tune the segmentation results so as to enhance segmentation accuracy of tumors. To verify the feasibility of the algorithm, the experiments were conducted on a private dataset provided by a medical institution, and the experimental Dice coefficient reached 0.876 on T2WI images, 0.851 on DWI images, respectively, indicating the existence of an important clinical guidance value for rectal tumor image segmentation algorithms. © 2023 IEEE.

关键词： Tumors

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Fast Convergence and Low Complexity Algorithm for Multichannel Adaptive Filtering

Fast Convergence and Low Complexity Algorithm for Multichann...

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Telecommunications and Intelligent systems (ICTIS), International conference on

作者： Mohamed Zerouali Mohamed Djendi Signal Processing and Image Laboratory (LATSI) University of BLIDA 1 Blida Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

In this research, we propose a new multi-channel adaptive filtering method with low complexity and optimized convergence rate. The basic strategy behind the proposed algorithm is to generalize the decorrelation technique used in FNLMS algorithm to the multi-channel case by estimating the prediction parameters for each channel to decorrelate the multi-channel input signals. In simulation we confirm the high performances of the proposed algorithm with comparison of the two dimensions multi-channel Affine Projection (multi-channel APA) and the multi-channel Normalized Least Mean Square (multi-channel NLMS) algorithms.

关键词： Adaptive systems Echo cancellers Filtering Filtering algorithms Prediction algorithms Decorrelation Complexity theory Telecommunications Intelligent systems Convergence

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