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

共 13657 条记录，以下是561-570 订阅

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Automatic Labeling of In-Situ Crystal images based on Lightweight Network Bisenetv2 43

Automatic Labeling of In-Situ Crystal Images based on Lightw...

引用

43rd Chinese Control conference, CCC 2024

作者： Yan, Luyao Fan, Ji Liu, Tao Wang, Hui Zhao, Zhenbang Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Dalian University of Technology Institute of Advanced Control Technology Dalian116024 China

ISBN: (纸本)9789887581581

To enhance the efficiency and accuracy of in-situ crystal image annotation for building up a model to monitor the crystal growth process during cooling crystallization, a deep learning based automatic labeling method is proposed by using the lightweight network Bisenetv2. The image segmentation is conducted by the lightweight network Bisenetv2, for which a hyperparameter optimization (HPO) and class balance training strategy is presented to improve the segmentation accuracy on in-situ captured images. The trained model is converted into the open neural network exchange (ONNX) format to reduce the implemental dependence on any specific or complex environmental configurations. Furthermore, a software package is developed to embed the ONNX format model for implementation. Additionally, a multi-stage image processing algorithm is provided to enhance image quality for analysis, based on the characteristics of in-situ crystal images captured during the crystallization process. Correction algorithms for poorly segmented crystal contours are also designed, resulting in a final mean intersection over union (mIOU) of 0.8216, with the automated annotation process taking approximately 1 second per image. Experimental results on in-situ images of β form L-glutamic acid (β-LGA) captured from different batches of crystallization validate the effectiveness and robustness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： image segmentation

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

3rd World Conference on Intelligent and 3D Technologies, WCI...

引用

3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

ISBN: (纸本)9789819791231

The proceedings contain 45 papers. The special focus in this conference is on Intelligent and 3D Technologies. The topics include: Multi-scale Point Cloud Shape Completion Network Based on Deep Learning;research on Application and Effect Evaluation of Product Innovation Management Based on Deep Learning;study on Deep Learning-Based Personalised Product Recommendation Model for Autonomous Question-and-Answer Robot;application of image Watermarking Technology Based on Deep Learning in Copyright Protection;research on the Combination of Building Structural Health Monitoring and Deep Learning image processing;electrical Equipment Prediction in a Variable Electromagnetic Field Using Deep Learning;state Monitoring and Fault Prediction of Wind Farm Transmission and Transformation Equipment Based on Deep Learning;application of Deep Learning algorithms in the Innovation Ecosystem of Electric Power;optimization Strategy for Inventory Management Based on Machine Learning;intelligent Design and Evaluation of Aging Adaptable Public Spaces Based on Deep Learning;performance Optimization and Acceleration of Machine Learning algorithms in Task Allocation of Mine Maintenance Robots;Application of Deep Learning to Improve the Performance of Automotive Electronic Control Unit (ECU);deep Learning-Based Scene Classification for Remote Sensing images;Improved Swarm Intelligence Optimization Algorithm Based on SL-Relu Activation Function Improvement Strategy and Its Application in Price Forecasting;Automatic Segmentation of Traumatic Penumbra in Rat Brain Based on Improved UNet++;A Brain Tumor Classification Method Based on ResNeXt-SESA Network;A LSTM Algorithm for Coastal City Cultural Scene Value Sustainable Development Forecast Improvement;pattern Recognition in Archive Analysis Using Data Mining;building Crack Detection Method Based on Convolutional Neural Network.

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A Live Demo of Single-Photon Imaging and Applications

A Live Demo of Single-Photon Imaging and Applications

引用

SIGGRAPH conference on Emerging Technologies

作者： Jungerman, Sacha Sundar, Varun Gupta, Mohit Univ Wisconsin Madison Madison WI 53706 USA

ISBN: (纸本)9798400705243

Single-photon sensors are a novel class of imaging sensors that are sensitive to the individual arrival of photons. In recent years, single-photon sensors have witnessed rapid growth in key sensor characteristics such as array resolution, which, for the first time, has made computer vision and imaging with these emerging sensors possible. This rapid growth has also spurred the development of algorithms that can harness the rich spatio-temporal scene information captured by single-photon sensors, which has led to several new imaging capabilities. These capabilities include imaging in challenging SNR conditions, high-dynamic range imaging, compensating extreme camera motion extents, and even providing the functionality of numerous imaging systems via post-capture processing. Our demo will showcase these exciting capabilities to a wide computer vision and graphics audience, and in doing so, make a case for the mainstream adoption of single-photon technology.

关键词： Single-photon camera single-photon avalanche diode quanta image sensors high dynamic range high-speed imaging low-light imaging motion compensation software-defined cameras.

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Quantum Computing in image processing 1

Quantum Computing in Image Processing

引用

1st International conference on Recent Developments in Electronics and Communication systems, RDECS 2022

作者： Sachdeva, Kumud Sachdeva, Rajan Gupta, Himanshu Department of Computer Science and Engineering Chandigarh University Punjab Gharuan140413 India

ISBN: (纸本)9781643683607

If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types. © 2023 The authors and IOS Press.

关键词： image enhancement

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Comparative Analysis of CNN Architectures for Enhancing Oral Cancer Detection Using Advanced image processing Techniques 8

Comparative Analysis of CNN Architectures for Enhancing Oral...

引用

8th IEEE International conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Rathi, Snehal Puranik, Atharva Pratham, Shaantanu Kulkarni, Vibhav Chincholkar, Harshvardhan Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350391770

To effectively diagnose oral cancer, sophisticated technologies like deep learning must be investigated. This is still a significant difficulty in medical diagnostics. To assess Convolutional Neural Network (CNN) architectures' efficacy in oral cancer detection, we compare and contrast them in this research. Specifically, we compare the performance of VGG19, ResNet50, ResNet101, and a standard CNN model using a dataset of Oral Cancer (Lips and Tongue) images (OCI). We use sophisticated image processing methods including data augmentation, contrast improvement, and noise reduction to improve the quality of the supplied data. We use accuracy, precision rate, recall rate, and F1-score as our assessment measures. Our findings show that the suggested CNN design outperforms VGG19 (72.97%), ResNet50 (79.73%), and ResNet101 (71.62%) in terms of accuracy, with a maximum of 87.10%. Moreover, our approach shows excellent recall, F1score, and accuracy, highlighting its efficacy in correctly detecting malignant areas in oral pictures. Through this comparative analysis, we provide valuable insights into the suitability of different CNN architectures for oral cancer detection, without the integration of specific optimization algorithms. Our findings highlight the potential of deep learning techniques to improve treatment outcomes and early diagnosis for patients with oral cancer, ultimately leading to the advancement of computer-aided cancer detection (CAD) systems. © 2024 IEEE.

关键词： Convolutional neural networks

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People Counting System Using OpenCV algorithms and Edge Computing for Safety Management

People Counting System Using OpenCV Algorithms and Edge Comp...

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2023 IEEE International conference on Smart Information systems and Technologies, SIST 2023

作者： Seitbattalov, Zhexen Y. Canbolat, Huseyin Atanov, Sabyrzhan K. Moldabayeva, Zhanar S. Kyzyrkanov, Abzal E. Maidanov, Adil K. L. N. Gumilyov Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan Ankara Yildirim Beyazit University Department of Electrical and Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350335040

The necessity of ensuring people's safety in urban conditions during gathering in a certain indoor or outdoor area, as well as during the operation of technical equipment, requires measurement of their number, weights, the distance between them, density and many other parameters. The collection of those parameters and analysis allows you to ensure the optimal numerical value of people per area and not exceed the critical peak value, ensuring the safety of people's health and life. However, automating those processes requires stable network bandwidth and enormous computing resources for data transferring to the data center and its processing, as well as applying artificial intelligence algorithms to the data and sending the results back to the end-user. In this paper, to avoid the disadvantages of cloud computing, we consider Raspberry Pi with a camera module as an edge device to develop an automated people-counting system based on using OpenCV algorithms and Edge Computing for Safety Management. The proposed system can be used for indoor or outdoor areas, during scaffolding works and lift's operation for controlling an optimal number of people. As a result of the application the edge computing, network bandwidth and computing resources of the data center have been optimized. © 2023 IEEE.

关键词： image processing

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Eight Points Geometric Constraint for Stereo Matching 9

Eight Points Geometric Constraint for Stereo Matching

引用

9th International conference on systems and Informatics, ICSAI 2023

作者： Wang, Yuanbin Xiamen Institute of Technology School of Data and Computer Science Xiamen361021 China

ISBN: (纸本)9798350383706

Stereo point matching is a critical and fundamental problem in 3D vision. Numerous algorithms have been proposed to solve this problem. Since images obtained from 3D to 2D projection of a 3D scene lose depth information of the 3D scene, there is no numerical geometric invariant between stereo images. So, we do not have similar algorithms that use geometric invariants for point matching as in the case of 2D to 2D projective transformations. However, it is discovered that geometric constraints can be used to restrict the search space and eliminate faulty correspondences. In this paper we derive a new mathematical constraint relating eight corresponding points in stereo images. It is a simple formula consisting of coordinates of the eight points that compute to zero for the eight points to be in correspondence. This constraint can be used as a preliminary necessity testing step before other time-consuming methods to speed up the matching procedure. © 2023 IEEE.

关键词： Stereo image processing

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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 ...

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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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