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visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

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International image processing, Applications and Systems conference (IPAS)

作者： Mohamed Gabr Hana Younis Marwa Ibrahim Sara Alajmy Wassim Alexan Faculty of MET The German University in Cairo Cairo Egypt Faculty of IET The German University in Cairo Cairo Egypt

ISBN: (纸本)9781665462204

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Wireless communication Performance evaluation visualization image coding Chaotic communication DNA Production

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The Potential Utility of image Descriptions: User Identity Linkage across Social Networks Based on MultiModal Self-Attention Fusion

The Potential Utility of Image Descriptions: User Identity L...

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IEEE International conference on Performance, Computing and communications (IPCCC)

作者： Yunfeng Li Gaopeng Gou Gang Xiong Zhen Li Mingxin Cui Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences

The task of user identity linkage across social networks aims to predict whether users from different social networks refer to the same person. This task plays a crucial role in cross-social network information dissemination and intelligent recommendations. However, existing user identity linkage tasks suffer from several challenges: 1) excessive reliance on social network topology, neglecting users’ visual modality information; 2) inadequate handling of noise in user feature data; and 3) ineffective fusion of users’ multimodal information. To address these issues, we investigated a method that utilizes heterogeneous multimodal posts, including user-generated text, images, and check-in messages, to achieve user identity linkage across social networks. We innovatively leveraged a pre-trained model for image-to-text conversion to further explore users’ image data and proposed an adversarial learning model based on the multimodal self-attention mechanism (AMSA). The AMSA model consists of four components: user feature extraction, user feature processing, user feature fusion, and adversarial learning. Specifically, AMSA initially employed advanced pre-trained models to extract features from multiple modalities of users, including images and text. Subsequently, it utilized multiple mechanisms, such as multi-head self-attention, to process data from each modality separately and then fused them into user representation vectors. Finally, AMSA employed adversarial learning to enhance the model’s learning capacity and mitigate semantic disparities in user information across different platforms. We conducted model performance evaluations on publicly available datasets, and experimental results demonstrated the superiority of the proposed AMSA model.

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A Garment Suggestion Engine Based on visual Similarity 28

A Garment Suggestion Engine Based on Visual Similarity

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28th Signal processing and communications Applications conference (SIU)

作者： Gokturk, Mehmet Kabakli, Bilal Yogurtcuglu, Hamza Gebze Tech Univ Dept Comp Sci Gebze Turkey Modanisa AS R&D Ctr Istanbul Turkey

ISBN: (纸本)9781728172064

In this study, a product suggestion engine has been developed for an e-commerce site portfolio focusing on garment items. Traditional collaborative filtering methods usually lack applicability due to high turnover rates in product lists. Thus, by focusing on visual similarity using deep leraning technique successful results were obtained and it has been concluded that application of this technique to real live e-commerce garment site will be suitable.

关键词： deep learning e-commerce image processing

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Compression and Transmission of 8K Stereoscopic VR Using VAE-GAN Latents and Standard Encoders

Compression and Transmission of 8K Stereoscopic VR Using VAE...

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IEEE conference on Wireless communications and Networking

作者： Sampreet Vaidya Hatem Abou-Zeid Diwakar Krishnamurthy Department of Electrical and Software Engineering University of Calgary Calgary AB Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Despite the heightened popularity of Virtual Re-ality (VR), streaming high-resolution stereoscopic VR remains a challenge. This is primarily due to significant bandwidth demands of high-definition VR content. While advanced deep neural networks (DNN s) have demonstrated the potential to outperform standard codecs, their integration into real-world transmission frameworks is complex and not directly compatible with current encoding standards. To bridge this gap, this paper proposes a novel technique for compressing and transmitting 8K stereoscopic scenes using Variational Autoencoder (VAE) GAN latents represented as 3-channel RGB scenes that can be transmitted via standard encoders. The proposed method reduces bandwidth requirements by 45.1 % across different 8K scenes while maintaining visual quality, highlighting the effectiveness of the approach. This study also investigates the impact of varying patch-sizes of input frames for model training and evaluate its influence on client-side reconstructions. We then explore various transmission configurations of latent frames. Our findings suggest that while residual transmission offers limited benefits for 3-channel latent frame compression, raw transmission consistently yields better results, particularly for texture-heavy scenes. To the best of our knowledge, this is the first such transmission study on 8K stereoscopic scenes for cloud-based VR, providing valuable insights for optimizing high-resolution VR streaming systems. Code: ***/sampreetucalgary07/8K-VR-compression.

关键词： Training Cloud computing visualization Solid modeling Stereo image processing Bandwidth Virtual reality Streaming media Generative adversarial networks Standards

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Deep Learning: A Detailed Analysis Of Various image Augmentation Techniques

Deep Learning: A Detailed Analysis Of Various Image Augmenta...

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Networking and communications (ICNWC), International conference on

作者： S. Swathi M. Rajalakshmi Vijayalakshmi Senniappan Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Department of Information Technology Coimbatore Institute of Technology Coimbatore India Department of Information Technology Park College of Engineering & Tech Coimbatore India

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses.

关键词： Deep learning Surveys Computer vision visualization Machine learning algorithms Taxonomy Feature extraction

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On 2D-3D image Feature Detections for image-To-Geometry Registration in Virtual Dental Model

On 2D-3D Image Feature Detections for Image-To-Geometry Regi...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Tang, Hui Hsung, Tai-Chiu Lam, Walter Y. H. Cheng, Leo Y. Y. Pow, Edmond H. N. Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Univ Hong Kong Fac Dent Hong Kong Peoples R China

ISBN: (纸本)9781728180687

3D digital smile design (DSD) gains great interest in dentistry because it enables esthetic design of teeth and gum. However, the color texture of teeth and gum is often lost/distorted in the digitization process. Recently, the image-to-geometry registration shade mapping (IGRSM) method was proposed for registering color texture from 2D photography to 3D mesh model. It allows better control of illumination and color calibration for automatic teeth shade matching. In this paper, we investigate automated techniques to find the correspondences between 3D tooth model and color intraoral photographs for accurately perform the IGRSM. We propose to use the tooth cusp tips as the correspondence points for the IGR because they can be reliably detected both in 2D photography and 3D surface scan. A modified gradient descent method with directional priority (GDDP) and region growing are developed to find the 3D correspondence points. For the 2D image, the tooth tips contour lines are extracted based on luminosity and chromaticity, the contour peaks are then detected as the correspondence points. From the experimental results, the proposed method shows excellent accuracy in detecting the correspondence points between 2D photography and 3D tooth model. The average registration error is less than 15 pixels for 4752x3168 size intraoral image.

关键词： 3D digital smile design image to geometry registration Tooth Shade Intraoral scan Range image

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Residential Real Estate image Classification for Property Valuation 28th

Residential Real Estate Image Classification for Property Va...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Nejad, Mehrdad Ziaee Naderpour, Mohsen Behbood, Vahid Ramezani, Fahimeh Lu, Jie 15 Broadway UltimoNSW2007 Australia

ISBN: (纸本)9783031859328

Residential real estate price is one of the key components of our economic developments and has also been a major concern of the public, bank industry, government, and investors. The accurate estimation of the sale price and its changes have an important role in the decision-making of related departments and organizations. In Australia, one of the biggest investments for people is in residential real estate. Therefore, many studies and research works have been carried out to build an automated valuation model to predict sale prices of residential properties accurately as much as possible. Automatic and accurate image classification of residential real estate plays an important role in property valuation and decision making of both sellers and buyers. It can be used in real estate online websites to organize the images for each property or used as a component in a visual decision support system for predicting the property sale prices based on property images. As convolutional image classification models show valuable performance in comparison with traditional models, a convolutional classification model is developed in this paper which creates a highly reliable classification component to be used in the corresponding research areas. The performance of the proposed model is investigated through a real dataset of New Sales Wales, Australia. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Multi-Sensor Fusion SLAM Based on Fast Euclidean Clustering to Filter Dynamic Targets

Multi-Sensor Fusion SLAM Based on Fast Euclidean Clustering ...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Ming Geng Yongxing Jia Xiaojian Qian Ziang Zhao College of Communications Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

R3LIVE is a new multi-sensor fusion SLAM framework, which mainly uses the three sensors of lidar, camera and IMU to realize the effective fusion of heterogeneous data through the error state iterative Kalman filtering method, so as to achieve the function of efficient and accurate simultaneous positioning and mapping. Specifically, this system uses lidar and IMU to construct laser inertial odometer (LIO) to construct the geometry of the external environment, and also it builds a visual inertial odometer (VIO) with a camera and IMU to reconstruct a dense RGB map of the environment. However, in the face of the environment where dynamic objects appear, the positioning accuracy of the system will be affected due to the point cloud of the sensor on the dynamic objects. Moreover, the continuous point cloud of the dynamic objects will cause continuous “ghost” in the dense RGB map constructed. In order to solve the above problems, a fast Euclidean clustering algorithm is proposed to filter out dynamic targets, that is, before the LIO system laser point cloud registration, the input point cloud is initially classified by using a fast Euclidean clustering algorithm, and the original point cloud is divided into ground points, background points and foreground points, among which the ground points and background points are usually considered static points. The foreground points may be either dynamic or static points. Then, the viewpoint visibility method is used for the foreground points to further verify the existence of dynamic points, and the dynamic points in the foreground points are filtered out. Finally, a widely distributed sparse extraction method is designed to solve the problem of excessive extraction of surface features in scenes with rich structure and texture. This enhances the real-time performance and robustness of the system in dynamic scenarios.

关键词： Point cloud compression visualization Accuracy Simultaneous localization and mapping Heuristic algorithms Clustering algorithms Filtering algorithms

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IEEE International Symposium on Parallel and Distributed processing with Applications and IEEE International conference on Ubiquitous Computing and communications (ISPA/IUCC)

作者： Hui Guo Di Xie Jiahao Pang Jie He Caixu Xu Xuejian Zhang Minglang Chen Faculty of Arts and Humanities Macao University of Science and Technology Wuzhou China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China college of Electronical and Information Engineering Wuzhou University Wuzhou China

At present, the self-similarity of images has received wide attention in many fields such as image compression. Most of the images have strong self-similarity through the human eye visual system observation. However, the special images with strong self-similarity for human eye observation such as biomicroscopy are not digitally reflected by a certain algorithm, this paper proposes a self-similarity coefficient algorithm that is applicable to special natural images such as biomicroscopy and aerial photography, which has high self-similarity coefficients for biomicroscopy and other images.

关键词： Photography image coding Microscopy visual systems Ubiquitous computing Biomedical imaging

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Stereo image Inpainting for UGVs Based on a Multistage Feature Alignment and Refinement Network 2

Stereo Image Inpainting for UGVs Based on a Multistage Featu...

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2nd International conference on Electronics, communications and Information Technology, CECIT 2021

作者： Yang, Xiaokang Li, Hengyu Liu, Jingyi Song, Tao Cui, Guohua Xie, Shaorong School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai201620 China

ISBN: (纸本)9781665437578

The binocular vision systems provide a wealth of visual information for UGVs. However, the dust, soil and water droplets in the environment can easily contaminate the camera lens, which will cause errors in the robot's visual perception system. However, the current GAN-based image inapinting methods usually generate wrong textures, which are quite different from the real scenes. To address this problem, we propose an inpainting method that uses stereo image information as prior knowledge. In this work, we first use a multistage feature alignment module to align the left and right feature maps, and use the dynamic fusion module to fuse the two features after each alignment stage. After that, we use the feature refinement module to refine the texture information of the feature maps to generate more refined details. Experiments show that our method surpasses previous work, and achieves a higher PSNR and SSIM evaluation metrics while running over 25 fps on single NVIDIA GeForce RTX 2080 Ti, which provides effective self-inpainting capabilities for the binocular vision system of UGVs to keep driving safety. © 2021 IEEE.

关键词： Stereo image processing

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