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

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Human Action Recognition using Attention EfficientNet 6

Human Action Recognition using Attention EfficientNet

引用

6th International conference on Signal processing and Information Security, ICSPIS 2023

作者： Karsh, Bhumika Laskar, R.H. Karsh, R.K. Nit Silchar Dept. of Ece Silchar India

ISBN: (纸本)9798350329599

The integration of Human Activity Recognition (HAR) within automated residences and surveillance systems has significantly elevated the significance of this field for advanced investigation. HAR entails the deployment of artificial intelligence (AI)-driven algorithms to autonomously discern human actions. Given the remarkable potency of deep learning (DL) methodologies in deciphering intricate tasks, their supremacy over conventional machine learning (ML) techniques has rendered DL-driven HAR increasingly prevalent. This study centers on the application of the EfficientNet transfer learning architecture with attention mechanism to the realm of HAR image recognition, specifically utilizing skeleton-based representations. The objective was to discern and evaluate the architecture's performance attributes in this context. Notably, our results unveiled an impressive classification accuracy of 99% for EfficientNet. These findings underscore the efficacy of the EfficientNet model in enhancing HAR capabilities, showcasing its potential for pivotal roles in automated settings. © 2023 IEEE.

关键词： image recognition

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Research on embedded system of image acquisition and real-time processing based on ZYNQ

Research on embedded system of image acquisition and real-ti...

引用

2022 International conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, ZongJie Peng, Liu School of Electronic Information Engineering Changchun University of Science and Technology China

ISBN: (纸本)9781510655171

In order to meet the demand for flexible acquisition of video images and real-time processing of images, the speed and efficiency of image processing are improved. This paper is an embedded system based on the ZYNQ-7000 series chip which integrates ARM+FPGA structure for image acquisition and real-time processing. An improved sharpening algorithm that can be used for edge detection in the image processing module is proposed, and the algorithm is integrated into the hardware through the HLS tool that comes with Vivado, which realizes the flexible acquisition and real-time processing of video image data. By studying image processing algorithms commonly used in embedded acquisition and processing systems, an improved sharpening algorithm is designed based on threshold segmentation and image filtering and applied in experiments. In terms of hardware, it has the advantages of low power consumption, high performance and flexibility. In terms of algorithm, it not only improves the speed of image optimization processing, but also has a large development space and application value. © 2022 SPIE.

关键词： Embedded systems

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Multi-scale target detection for airfield visual navigation of taxiing aircraft 4

Multi-scale target detection for airfield visual navigation ...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Luo, Qijun Chen, Jiagang Zhang, Xuerui Zhang, Tao Civil Aviation University of China Tianjin Intelligent Signal and Image Processing Experiment Tianjin300300 China

ISBN: (纸本)9798350394375

In the context of visual navigation-based civil aircraft assistance systems, the scene target scales captured by onboard cameras exhibit significant variations, and the computational power of airborne platforms is limited. To promptly and accurately identify threat targets in airborne scenic images, a lightweight multi-scale object detection algorithm based on improved YOLOv5s is proposed. Firstly, on the foundation of the Weighted Bidirectional Feature Pyramid Network (BIFPN), a Coordinate Attention (CA) mechanism is integrated to design the CA-BIFPN feature fusion network. This enhances feature representation for small targets and improves the model's learning capability for multi-scale targets. Secondly, a cross-level sub-network module is employed to lightweightly process the neck section, reducing the model's parameter count and thereby improving the overall network's detection speed. Experimental results on a self-constructed scene image dataset demonstrate that the proposed method achieves a detection frame rate of 89FPS, an increase of 5fps. The average precision mean (mAP) on the test set is 70.6%, a 3.4% improvement over the original YOLOv5s. The detection accuracy surpasses typical algorithms such as YOLOv6, YOLOv7, YOLOX, and Faster-RCNN. © 2024 IEEE.

关键词： image enhancement

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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...

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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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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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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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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 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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