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

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

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

引用

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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image Forgery Detection: A Review 4th

Image Forgery Detection: A Review

引用

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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An Event-Driven Approach to the Recognition Problem in Video Surveillance System Development 32

An Event-Driven Approach to the Recognition Problem in Video...

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32nd conference of Open-Innovations-Association-FRUCT

作者： Bazhenov, Nikita Rybin, Egor Korzun, Dmitry Petrozavodsk State Univ PetrSU Petrozavodsk Russia

ISBN: (数字)9789526924489

ISBN: (纸本)9789526924489

Many video surveillance systems (VSS) have been already developed for various application domains. Such systems are based on well-elaborated recognition algorithms of Artificial Intelligence (AI) and implemented as Ambient Intelligence (AmI) services in Internet of Things (IoT) environments. In particular, algorithms support such smart VSS functions of video data processing as human detection, human identification, object location within an image, human activity recognition. Many software tools have been developed to implement various recognition algorithms for VSS development. In this paper, we consider the following VSS development problems: a) a generic model of events in video data for a given problem domain, b) a hardware-software architecture for data processing with existing recognition algorithms, and c) a model to construct a required smart VSS function using existing software tools. We introduce our event-oriented approach to solve the above VSS development problems. The approach is experienced in several use cases. Our experimental study shows the applicability of the proposed approach in terms of the accuracy and performance.

关键词： Technological innovation Face recognition Software algorithms Computer architecture Data processing Video surveillance Data models

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Diagnosis of Pneumonia using image Recognition Techniques 7

Diagnosis of Pneumonia using Image Recognition Techniques

引用

7th International conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sangeetha, S. Suruthika, S. Keerthika, S. Vinitha, S. Sugunadevi, M. Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India

ISBN: (纸本)9798350397253

Pneumonia is a severe and fatal disease caused by viral or bacterial sepsis in human lungs. Early detection of pneumonia is critical for successful treatment. Deep learning algorithms that use many layers to interpret a boundary of data have obtained the best results in a variety of disciplines, particularly in the documenting and sorting of various diseases. As a result, there is an increasing need to design and develop a pneumonia detection system by using deep learning methods with an ability to evaluate chest CT-scan imageries and aid in the pneumonia exposure process. The proposed framework contains three phases: feature extraction, image classification, and image pre-processing. As a consequence, the projected Compared to other deep-learning models that have already been trained, such as (ResNet 50) and SVM, pneumonia diagnosis using the CNN model performed better in terms of accuracy and reliability. As a result, the efficient performance of the proposed Convolutional Neural Network centred pneumonia perception model in every parameter can provide valuable assistance to patients' care and reduce the human error rates. © 2023 IEEE.

关键词： Convolutional neural networks

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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 14

Advancing Fingerprint Recognition Quality Assessment: Introd...

引用

14th Indian conference on Computer Vision, Graphics and image processing, ICVGIP 2023

作者： Jaiswal, Prateek Koner, Arka Namboodiri, Anoop M. CVIT IIIT Hyderabad India India

ISBN: (纸本)9798400716256

In the field of biometric security, the quality assessment of fingerprint images is paramount for boosting the accuracy of fingerprint recognition systems. These systems are fundamental for the secure and efficient authentication and identification of individuals. Our research presents FRBQ (Fingerprint Recognition-Based Quality), an innovative quality metric designed to navigate the limitations of the NFIQ2 model. FRBQ exploits deep learning algorithms in a weakly supervised setting and utilizes matching scores from DeepPrint, a Fixed-Length Fingerprint Representation Model. Each score is paired with labels indicating the robustness of fingerprint image matches. However, in a fully referenced setting, these labels can be subjective, lacking a clear definition of what "image quality" inherently means. This weakly labeled approach strives to capture diverse perspectives on image quality, potentially making it a more encompassing metric. In comparison to NFIQ2, our research showcases the superior performance of the FRBQ model. It not only correlates better with recognition scores but also effectively evaluates challenging images that NFIQ2 struggles with. Validated by the esteemed FVC 2004 dataset, FRBQ proves its efficacy in fingerprint image quality assessment. This study underscores the transformative potential of AI in biometrics, emphasizing its capability to capture details that traditional methods might overlook. Our work stresses the critical role of precise quality assessment in the evolution of fingerprint recognition systems. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image quality

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Stopping Method for Iterative Scene Identification in an image Stream 18

Stopping Method for Iterative Scene Identification in an Ima...

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18th International Scientific and Technical conference on Dynamics of systems, Mechanisms and Machines, Dynamics 2024

作者： Muranov, A.N. Titova, A.P. Nogmov, M.H. Laboratory No 3 Institute of Design-Technology Informatics of RAS Moscow Russia

ISBN: (纸本)9798331509019

As part of solving a set of computer vision problems when analyzing video streams, a fairly popular task is to effectively stop the process of identifying scenes or objects. Arresters are one of the main problems in systems that implement online computer vision. This is explained by the fact that period of time spent obtaining results in video analytics systems is no less important than the reliability of the analysis results. In this regard, the conducted research focused on the problem of stopping when identifying scenes in a video stream. This requires detailed stopping algorithms for identifying scenes in video streams with a decision to arrest iterations, taking into account that it is not possible to generally assess the accuracy of the identification result. In this work, a list of properties of monotonic stopping functions was compiled, and an original algorithmic technique for solving stopping problems was proposed. The testing results demonstrated the high efficiency of the proposed method for identifying the text part of documents. © 2024 IEEE.

关键词： Video streaming

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Review Paper on Glaucoma Detection Using Machine Learning 5th

Review Paper on Glaucoma Detection Using Machine Learning

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5th International conference on Energy systems, Drives and Automations, ESDA 2022

作者： Jha, Kushal Gokharu, Naman Mathur, Rohan Bhandari, Sachin Department of Computer Engineering Mukesh Patel School of Technology Management and Engineering SVKM’s NMIMS Shirpur Campus India

ISBN: (纸本)9789819701537

Glaucoma is one of the leading causes of vision loss worldwide. Glaucoma cannot be cured in its advance stages. So, early detection of disease has become an important factor in the medical field. Numerous studies quickly became clear that using different image processing methods, the retinal fundus picture could be uncovered. In this study, many automated glaucoma detection techniques were thoroughly reviewed various papers were compared on the basis of the methodologies they adopted for detecting glaucoma from 2D fundus images created using CDR. 85% of glaucoma cases can be accurately detected by the majority of machine learning algorithms. First, image segmentation techniques like Elliptical Hough transform and edge detection gave the region of interest, i.e. optic disc and cup. These extracted images were then given to the machine learning and deep learning models to detect presence of glaucoma in the fundus image of the eye. The most significant deep learning, machine learning, and transfer learning methods for analysing retinal images were reviewed, along with their benefits and drawbacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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An improved visual reality experience for gaming applications using visual image processing 15

An improved visual reality experience for gaming application...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ruby, E.D. Kanmani Titus, Anita Anitha, R. Vijayakumari, P. Ravi, S. Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Jeppiaar University Department of Ece Chennai India S.A. Engineering College Department of Ai & Ds Chennai India Sairam Engineering College Department of Ece Chennai India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

Digital and augmented truth has increased in recent years, with several packages in various industries. Gaming is one such industry that has benefitted significantly from those technologies, supplying customers with an immersive and interactive revel. But there's nevertheless room for improvement, especially in visible photograph processing. Cutting-edge digital and augmented fact systems depend upon pre-rendered three-D picks, which could bring a lower fine and much less realistic visual experience. This is especially obtrusive in rapid-paced gaming packages, where photographs may appear pixilated or need more elements. To deal with this difficulty, our research proposes an advanced visual fact enjoyed by gaming applications using superior visible picture processing techniques. Our method mainly includes the realtime processing of visual data to create exceptional realism in the digital environment. This may encompass strategies, texture mapping, dynamic lights, and superior rendering algorithms. © 2024 IEEE.

关键词： Applications. Exceptional Augmented Immersive Improvement Supplying

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