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

共 16443 条记录，以下是1051-1060 订阅

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Elevating Perception: Unified Recognition Framework and Vision-Language Pre-Training Using Three-Dimensional image Reconstruction 2

Elevating Perception: Unified Recognition Framework and Visi...

引用

2nd International conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Wang, ZhiQiang Joshi, Aryan Zhang, GuiYing Ren, WenJia Jia, Feng Sun, xiaoHang Tianjin University of Technology and Education Dept. of Information Tech & Engineering Tianjin China Tianjin Ai Shen de Si Technology Co. Ltd Dept. of Information Tech & Engineering Tianjin China

ISBN: (纸本)9798350360363

This research project explores a paradigm shift in perceptual enhancement by integrating a Unified Recognition Framework and Vision-Language Pre-Training in three-dimensional image reconstruction. Through the synergy of advanced algorithms from computer vision & language processing, the project tries to enhance the precision and depth of perception in reconstructed images. This innovative approach holds the potential to revolutionize fields such as medical imaging, virtual reality, and computer-aided design, providing a comprehensive perspective on the intersection of multimodal data processing and perceptual advancement. The anticipated research outcomes are expected to significantly contribute to the evolution of technologies that rely on accurate and contextually rich three-dimensional reconstructions. Moreover, the research aims to reduce the constant need for new datasets by improving pattern recognition through 3D image patterning on backpropagation. This continuous improvement of vectors is envisioned to enhance the efficiency and accuracy of pattern recognition, contributing to the optimization of perceptual systems over time. © 2023 IEEE.

关键词： image enhancement

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Unconventional to Automated Attendance Marking Using image processing 4th

Unconventional to Automated Attendance Marking Using Image P...

引用

4th International conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Pradhan, Abhiujjwal Pal, Ritu Kumar, Sachin Koli, Sakshi Kalra, Bharti Nawaz, Waris Computer Science Department Tula’s Institute Dehradun Dehradun India

ISBN: (纸本)9789811998874

The traditional method of raising your hand in a classroom to say "present ma’am" or "yes ma’am" or whatever other things you would say is kind of fading away, image processing is becoming increasingly important in the digital world. Magicians play an important function in today's information era. Visual processing is necessary in the area of biometrics to identify a individual whose biometric image has already been saved in a database. image based biometrics, such as faces, biometrics, and eyes, need image processing and pattern recognition algorithms. To perform correctly, an image-based biometric system requires a sample image of the user's biometric that is highly clear and unadulterated. This study proposes the development of a system that automated stamps students’ attendance using automated image render ways such as facial detection and identification. Face recognition is challenging due to factors such as face pattern, orientation, shape, varied patterns, and the complexity of face expression. Using multiple datasets, the system is taught to identify the face-representing pattern (positive pattern of face) and set it apart from the surroundings (critical pattern of facial expression) environment. The major goal is to provide an autonomous platform for recognising faces in video, as well as documenting student attendance by identifying them from their various face patterns. This aids in the automatic maintenance and management of the attendance system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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Propensity Score Alignment of Unpaired Multimodal Data 38

Propensity Score Alignment of Unpaired Multimodal Data

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： xi, Johnny Osea, Jana Xu, Zuheng Hartford, Jason Department of Statistics University of British Columbia Vancouver Canada Valence Labs Montreal Canada Valence Labs London United Kingdom

Multimodal representation learning techniques typically require paired samples to learn shared representations, but collecting paired samples can be challenging in fields like biology, where measurement devices often destroy the samples. This paper presents an approach to address the challenge of aligning unpaired samples across disparate modalities in multimodal representation learning. We draw an analogy between potential outcomes in causal inference and potential views in multimodal observations, allowing us to leverage Rubin's framework to estimate a common space for matching samples. Our approach assumes experimentally perturbed samples by treatments, and uses this to estimate a propensity score from each modality. We show that the propensity score encapsulates all shared information between a latent state and treatment, and can be used to define a distance between samples. We experiment with two alignment techniques that leverage this distance-shared nearest neighbours (SNN) and optimal transport (OT) matching-and find that OT matching results in significant improvements over state-of-the-art alignment approaches in on synthetic multi-modal tasks, in real-world data from NeurIPS Multimodal Single-Cell Integration Challenge, and on a single cell microscopy to expression prediction task. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Learning-Based image Recognition for Electronic Components Identification 5

Deep Learning-Based Image Recognition for Electronic Compone...

引用

5th IEEE International conference on Recent Trends in Computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Royappa, Angelina Monga, Sandeep Geetha, H. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India S.A. Engineering College Department of ECE Tamil Nadu Chennai India Oriental Institute of Science and Technology Department of Computer Science and Engineering India St.Joseph's College of Engineering OMR Semmenchery Department of Mathematics Tamil Nadu Chennai India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

The use of deep learning techniques in the field of picture recognition for the purpose of identifying electronic components. Because of the growing complexity and variety of electronic devices, it is essential for manufacturing, maintenance, and quality control to be able to identify components in a way that is both efficient and accurate. This is accomplished through the utilization of deep learning algorithms, the utilization of convolutional neural networks (CNNs), and advanced image processing techniques in order to improve recognition skills. The suggested model displays higher accuracy in differentiating diverse electronic components, overcoming problems such as differences in size, orientation, and lighting conditions. Moreover, the model succeeds in overcoming these challenges effectively. Based on the findings, it appears that deep learning-based image recognition provides a strong solution for automating the identification process in electronic component analysis. This, in turn, contributes to better efficiency and reliability in the electronics industry. This research makes a contribution to the expanding field of computer vision and highlights the potential of deep learning in the advancement of electrical component identification systems. © 2024 IEEE.

关键词： image enhancement

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TOPSIS Cooperative Positioning Quality Evaluation Cloud Model Integrating EWM and AHP

TOPSIS Cooperative Positioning Quality Evaluation Cloud Mode...

引用

2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： Zhang, Cunle Wang, Haonan Tang, Chengkai Lian, Baowang School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350316728

The intelligent development of the integration of space, space, and sea cannot be achieved without navigation and positioning services. The current collaborative positioning solutions are rich and complex, with varying evaluations of positioning performance, and there is an urgent need to evaluate them. This study proposes evaluation methods from the nodes, links, algorithms, and hardware of collaborative positioning systems, provides quality evaluation levels, and constructs an evaluation architecture that integrates tomography analysis (AHP), entropy weight analysis (EWM), ideal advantage and disadvantage distance analysis (TOPSIS), and cloud model theory (CM). This framework has been evaluated and applied as an example through the collaborative positioning experiment of unmanned aerial vehicle clusters. Combining AHP-EWM with multi-dimensional TOPSIS cloud model for evaluation provides a scientific new approach for performance evaluation and provides a scientific basis for decision-making and improvement of actual positioning schemes. © 2023 IEEE.

关键词： Decision making

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Hyper-SD: Trajectory Segmented Consistency Model for Efficient image Synthesis 38

Hyper-SD: Trajectory Segmented Consistency Model for Efficie...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ren, Yuxi xia, xin Lu, Yanzuo Zhang, Jiacheng Wu, Jie xie, Pan Wang, xing xiao, Xuefeng ByteDance China

Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation;and ii) ODE Trajectory Reformulation. However, these approaches suffer from severe performance degradation or domain shifts. To address these limitations, we propose Hyper-SD, a novel framework that synergistically amalgamates the advantages of ODE Trajectory Preservation and Reformulation, while maintaining near-lossless performance during step compression. Firstly, we introduce Trajectory Segmented Consistency Distillation to progressively perform consistent distillation within pre-defined timestep segments, which facilitates the preservation of the original ODE trajectory from a higher-order perspective. Secondly, we incorporate human feedback learning to boost the performance of the model in a low-step regime and mitigate the performance loss incurred by the distillation process. Thirdly, we integrate score distillation to further improve the low-step generation capability of the model and offer the first attempt to leverage a unified LoRA to support the inference process at all steps. Extensive experiments and user studies demonstrate that Hyper-SD achieves SOTA performance from 1 to 8 inference steps for both SDXL and SD1.5. For example, Hyper-SDXL surpasses SDXL-Lightning by +0.68 in CLIP Score and +0.51 in Aes Score in the 1-step inference. © 2024 Neural information processing systems foundation. All rights reserved.

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image Denoising Using Weight Model

Image Denoising Using Weight Model

引用

2022 International Russian Automation conference, RusAutoCon 2022

作者： Shleymovich, Mikhail Safina, Rimma Kazan National Research Technical University Named after A.N. Tupolev-KAI Department for Automated Systems for Information Processing and Control Kazan Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The article deals with the issues of primary image processing in computer vision-based systems. The main approaches to solving the problems of image enhancement and restoration, including using wavelet transformations, are described. Filtering algorithms based on threshold processing of the detailing coefficients at various levels of the wavelet decomposition are considered. An approach is proposed based on the construction and analysis of a weight model that reflects the importance of image elements in terms of their contribution to the total energy. A method for constructing a multiscale weight model, a procedure for removing noise in an image using it, and the results of experiments on processing noisy test images are given. It is shown that using the proposed procedure, it is possible to ensure the removal of Gaussian noise with a large standard deviation value. © 2022 IEEE.

关键词： image reconstruction

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Degraded Data Enhancement Based on Regional Similarity Fusion 6

Degraded Data Enhancement Based on Regional Similarity Fusio...

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6th International conference on Information Communication and Signal processing, ICICSP 2023

作者： Ding, Bosheng Zhang, Ruiheng Xu, Lixin Wang, Haichao Liu, Yumeng Zhao, Yijing Su, Yi School of Mechatronical Engineering Beijing Institute of Technology Beijing China Anhui Dongfeng Electromechanical Technology Co. Ltd Research and Development Department Heifei China Institute of Software Chinese Academy of Sciences Beijing China Institute of Software University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350339994

The scattering and absorption effect of dust particles on light leads to low contrast and serious color offset of visible light images obtained in sand-dust weather, which affects the reliability of outdoor visual applications such as traffic safety and monitoring systems. Due to complex scene structure and difficult parameter estimation, the existing sand-dust image enhancement methods can not effectively extract the semantic components of the image, resulting in unreal colors and blurred details of the enhanced image. Therefore, we propose a two-stage sand image enhancement method based on regional similarity fusion. Firstly, the gray distribution of the input dust image is compensated to recover the potential information in the scene, and two sub-images with color balance and high contrast are derived. Then, regional similarity calculation, weight allocation, and image fusion are carried out to generate the final clear image based on the regional similarity fusion strategy. The experimental results show that the proposed method can effectively restore the potential features in the dust scene, and the visible edge number ratio (e-score) and edge gradient ratio (r-score) have increased by 0.24 and 0.47 respectively. © 2023 IEEE.

关键词： degraded data image enhancement regional similarity fusion

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image Forgery Detection: A Review 4th

Image Forgery Detection: A Review

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4th International conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Vijayakumar, Preethi Mathew, Elizabeth Gayathry Devi, M. Monisha, P.T. Anjali, C. John, Jisha Department of CSE Mar Baselios College of Engineering and Technology Kerala Trivandrum India

ISBN: (纸本)9789811974014

images can now be easily modified due to advances in digital image processing. image forgery is the process of manipulating or changing a digital image in order to conceal vital information. Nowadays, as technology advances and the performance of low-cost computers improves, forgery in images has rapidly increased. Fraudulent images are quite impossible to recognize and detect through human eyes. The process of modifying fake images has been made extremely easy with powerful editing tools that are available for free. The software used works so well that it’s tough to tell the difference between legitimate and faked photos. Because digital photographs and images are used in so many locations, detecting digital image counterfeiting is critical as it can be a misuse of technology and image authenticity is essential in various social platforms. There are several forgery techniques developed to find the authenticity of images. This paper will benefit researchers in comprehending current algorithms and strategies in this sector, with the goal of eventually developing new and more efficient detection algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Research on Radar Emitter Signal Classification Technology based on SqueezeNet Lightweight Network 3

Research on Radar Emitter Signal Classification Technology b...

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3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Jiang, Hu Wang, Wei Wu, Juan Chen, xi Han, Ruoyu Zhang, Zhiyong Shi, Zhan Li, Pengfei State Key Laboratory of Mechatronics Engineering and Control Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510670945

With the rapid advancement of information technology, the modern battlefield is characterized by a highly complex electromagnetic environment. Radar radiation sources exhibit wide-ranging parameter variations and strong random characteristics, presenting formidable challenges to the signal selection of radar radiation sources in missile-borne countermeasure systems. This paper addresses the issue of reliable identification and selection of radar source signals by on-board countermeasures systems. Through the analysis of source signal characteristics, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) method is employed for time-frequency analysis to extract the time-frequency features of the source signals. Furthermore, a lightweight network based on SqueezeNet is implemented to achieve high-precision source signal selection. The results demonstrate that, when the SNR of the source signals is greater than 0dB, the network model achieves a recognition accuracy above 94.59%. The selection accuracy is comparable to that of the Convolutional Neural Network (CNN), thereby meeting the requirements of on-board countermeasure systems for reliable selection of radar source signals. The analysis confirms that under low signal-to-noise ratio conditions, noise significantly affects the network's selection accuracy by impacting the time-frequency clarity of the modulation signals. © 2023 SPIE.

关键词： Wigner-Ville distribution

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