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Polarization Fusion Algorithm Based on NSCT Decomposition and Improved Dual-Channel PCNN Model 4

Polarization Fusion Algorithm Based on NSCT Decomposition an...

引用

4th international conference on computer vision and Data Mining, ICCVDM 2023

作者： Hu, Chao Fan, Bin Bian, Jiang Zhong, Shuo Wang, Lun Hou, Mengxia Qi, Bo NationalKey Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu610209 China Chengdu610209 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510674448

Polarization imaging is capable of effectively showcasing the polarization properties of objects while also mitigating the impact of strong light and enhancing the visibility of weak light. As a result, it compensates for the limitations of intensity imaging in low-light environments. To fully utilize the advantages of intensity and polarization images, this paper proposes a method that combines NSCT decomposition and an improved dual-channel PCNN model. the method enhances the traditional PCNN model by extending the input to dual channels, employing adaptively calculated parameters, and applying adaptive linking weights to the high- and low-frequency images obtained from NSCT decomposition. the high-frequency image utilizes the MSMG operator, while the low-frequency image employs a weight map controlled by multiple parameters. this method is utilized for fusion processing of intensity and polarization *** conducting comparative experiments with six commonly used algorithms, the results demonstrate the superior performance of this method in terms of preserving texture details, providing high-quality images, achieving information-rich fusion, generating fused images with rich grayscale levels, and maintaining structural similarity. In summary, the proposed method exhibits significant advantages in fusing intensity and polarization images. © 2024 SPIE.

关键词： image fusion

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Detection of Potholes Using image processing Method 4th

Detection of Potholes Using Image Processing Method

引用

4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Norhairi, Muhammad Zulkifli Bin Abdullah Sazali, Norazlianie Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia

ISBN: (纸本)9789819988181

Potholes are a common problem on roads, caused by weather, vehicle activity, and poor maintenance. Potholes can be hazardous for drivers, cars, and motorcycle riders. Potholes are often filled with asphalt or concrete. A methodology for automatically identifying potholes on road surfaces using computer vision methods is potholes detection utilizing image processing. this technique can be used to improve road maintenance by quickly locating potholes, enabling early repairs, and lowering the risk to drivers and their cars. this study emphasizes a Gaussian noise filtering technique for the developed infrastructure of image pre-processing stage. thus, this study also suggests four methods for segmentation detecting potholes in images: image thresholding (Otsu), Canny edge detection, K-means clustering, and fuzzy C-means clustering. the effectiveness of the different image segmentation techniques was tested in MATLAB 2019a, and the results were generated in terms of accuracy and precision. the results were compared with each other to draw a conclusion on their viability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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An automatic system for picking up table tennis balls based on machine vision 4

An automatic system for picking up table tennis balls based ...

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Tan, Linyue School of Mechanical Engineering Dalian University of Technology No.2 Ling Gong Road Liaoning Da Lian116000 China

ISBN: (纸本)9781510672505

this article presents the establishment of a machine vision system intended for the retrieval of table tennis balls within a training hall. Capturing images of table tennis balls of various colors in the RGB color format was achieved through the utilization of a high-resolution camera affixed to the pick-up robot. To counteract the influence of environmental lighting conditions in the training hall, the acquired RGB images were converted into the HSI color format. Subsequently, image segmentation was executed based on predefined hue and saturation thresholds, enabling the precise identification and categorization of table tennis balls. the utilization of Canny edge detection and Hough circle detection methodologies was employed to designate the positions of the table tennis balls. through analysis of circle centers and quantities, the specific locations of the table tennis balls were ascertained for the pick-up robot. this automated system, grounded in machine vision, holds the potential to significantly enhance the level of automation involved in the process of collecting table tennis balls. © 2023 SPIE.

关键词： computer vision

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Information recognition of Printed Quantum Dots Matrix image under Uncorrected Conditions 4

Information Recognition of Printed Quantum Dots Matrix Image...

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4th international conference on Robotics and computer vision, ICRCV 2022

作者： Qiu, Yingying Cao, Peng Wang, Mingfei Beijing Institute of Graphic Communication Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipment Beijing China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

Printed quantum dots (PQDs) record information and load it into complex images to realize printing information anti-counterfeiting. A dots extraction algorithm was proposed for phone to read this image's information. After preprocessing the captured image with adaptive Otsu method, the dispersion of local pixel gray distribution was calculated and the tilt angles of the lattice was estimated. the PWM method was used to modulate the projection waveforms of the image on the X and Y axes to determine the spacing between points and grided the dot matrix. Finally, the dot matrix data was extracted in grid units. the algorithm has been measured by mobile app, the scanning and reading speed is within 300ms. © 2022 IEEE.

关键词： Semiconductor quantum dots

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Investigating the Relationship between CrB Phase through image processing and Mechanical Properties of Metal 3D Printed Alloy 4

Investigating the Relationship between CrB Phase through Ima...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Park, Dong Yong Park, Hyeon Jeong Lee, Yoon Sun Korea Institute of Industrial Technology Advanced Mobility Components Group Daegu Korea Republic of

ISBN: (纸本)9798350395914

the relationship between the quantified CrB phase via image processing method and the mechanical properties of 3D printed SUS316L with boron was examined. Nine experimental conditions, i.e., boron content, feed rate, laser power, and scan speed, were designed by Taguchi method. the formation of CrB phase in 3D printed alloy with nine process conditions was quantified using image processing method. the image processing algorithm was based on the intensity difference between the regions of CrB phase and others. the CrB phase directly affects the mechanical properties of the 3D printed stainless steel. the quantity of CrB phase and mechanical properties depend on the processing conditions. the mechanical properties for tensile strength and micro hardness with processing conditions were measured. the same processing conditions were used for both quantification of the CrB phase and the mechanical properties test. the relationship between the quantified CrB phase and the mechanical properties was identified. As is generally known, a high amount of CrB phase deteriorates the mechanical properties of materials. therefore, optimization of the processing conditions is crucially required to achieve the desired mechanical properties. With the signal the signal-to-noise (S/N) ratio analysis, the S/N ratio results for the quantified CrB phase were compared with that for the measured mechanical properties. the comparison through S/N ratio analysis contributes to determination of the influence of the CrB phase on the mechanical properties. Also, the comparison helps in finding of the desired mechanical properties. © 2024 IEEE.

关键词： Tensile strength

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Deep Learning-based Classification of Cataract Infected Eyes and Normal Eyes using Convolutional Neural Network 4

Deep Learning-based Classification of Cataract Infected Eyes...

引用

4th international conference on Expert Clouds and Applications, ICOECA 2024

作者： Ashitha Sandrin, A. Denisha, M. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385793

Cataract, a common eye disease characterized by clouding of the natural lens of the eye, is a serious threat to visual health. If left untreated, they can lead to blurred vision and even blindness, underscoring the importance of timely intervention. To address the urgent need for accurate cataract diagnosis, this study provides an improved approach using convolutional neural networks (CNN) to distinguish cataract eyes from eyes with normal vision. Because cataracts are the most common cause of visual impairment worldwide, recognition and accurate classification are critical for prompt intervention and mitigation of potential harms. Taking advantage of recent advances in deep learning, especially CNNs, this method introduces a new feature extraction technique that uses hyperparameter optimization. the systematic exploration of the hyperparameter space determines the optimal configuration that maximizes the efficiency of the classification algorithm to distinguish cataract from normal eyes. Experimental validation on a comprehensive fundus image dataset demonstrates the effectiveness of this approach, achieving robust performance and high classification accuracy. A deep learning approach with optimization techniques will improve the efficiency and accuracy of cataract diagnosis, offering promising solutions to alleviate the global burden of vision loss and blindness. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning-based Modular Framework for Risk Mitigation through Crowd Counting 4

Deep Learning-based Modular Framework for Risk Mitigation th...

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4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Dhanalakshmi, M. Vyshali Rao, K.P. Taseen, Rakheeba Shylaja, D.N. New Horizon College of Engineering Dept of CSE Bangalore India JSS Science and Technology University Dept of ISE Mysore India School of Computer Science and Engineering Presidency University Bangalore India Bangalore Technological Institute Dept of CSE Bangalore India

ISBN: (纸本)9798350375466

Crowd counting, a crucial task in visual surveillance and automated public monitoring, entails accurately determining the number of individuals within a given image. While significant progress has been made in this domain, existing solutions often exhibit limitations, such as requirements for individuals to be in motion, simplistic backgrounds, or high image resolutions. However, real-world scenarios typically involve both moving and stationary individuals, complex backgrounds, and videos with relatively low resolutions in visual surveillance systems. Consequently, achieving accurate crowd counting and human identification in such contexts remains a significant challenge. the primary objective is to develop a robust vision system capable of monitoring activities within a site over extended durations. To address these challenges, we propose an innovative crowd counting application leveraging artificial neural network methodologies, particularly utilizing the latest iteration of the YOLO (You Only Look Once) framework. this approach operates on live video streams in real-time, offering instantaneous analytics that can substantially enhance security measures and inform infrastructure development initiatives. By harnessing the power of deep learning and real-time analytics, our proposed framework aims to overcome the limitations of existing crowd counting methods and provide actionable insights for risk management applications. © 2024 IEEE.

关键词： Risk management

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Intelligent Models Equivalent to the Level of Radiologists for Multi-Label Lesion Detection in Chest X-Rays 4

Intelligent Models Equivalent to the Level of Radiologists f...

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4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Sun, Chenrui Chen, Chunyi Yao, Weixun Wu, Linghong College of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

In the field of radiology, the analysis of X-ray images serves as an essential tool for diagnosing thoracic diseases. With the rapid evolution of deep learning technologies, automated image analysis methods have been extensively integrated, aiming to significantly enhance the work efficiency and diagnostic precision of radiologists. Nonetheless, existing deep learning models still encounter a series of challenges when processing abnormal chest X-ray images, including the similarity of multiple categories of lesions, the diversity of scales, and the difficulty in detecting small target lesions. these issues severely hinder the advancement of model performance. To surmount these limitations, this study introduces an model for the detection and localization of multiple diseases from abnormal chest X-ray images. the model incorporates an advanced CNN-Swin Transformer module, which integrates global and local context information and employs a pixel-level self-attention mechanism through a moving window mechanism, effectively capturing multi-scale features of the images. Furthermore, the model introduces a channel and spatial permutation attention allocation mechanism, significantly enhancing the recognition of salient features in key areas and thereby improving detection accuracy. We evaluated the proposed model on the VinDr-CXR dataset, and compared with the state-of-the-art baselines, it achieved an increase of 2.3% in mean Average Precision (mAP), demonstrating the effectiveness of our approach in advancing the field of automated chest disease detection. © 2024 IEEE.

关键词： Radiology

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial recognition;crime Data Analysis Using Machine Learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with computer vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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Revolutionizing power line asset detection: A YOLOv8-Driven lightweight algorithm for UAV image recognition 4

Revolutionizing power line asset detection: A YOLOv8-Driven ...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Liu, Chunyao Chen, Zhiyu Guo, Jianwei Liu, Gang School of Computer Science and Engineering Changchun University of Technology Changchun130012 China Jilin Province Data Service Industry Public Technology Research Centre Changchun130012 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

the maintenance and inspection of power lines is key to ensuring their normal operation and maintaining an uninterrupted power supply for various human activities. Traditional methods to detect power line assets usually detect only a small number of assets, and face the challenge of low inspection accuracy and high computational resources. this study proposes a power line asset detection algorithm designed based on the YOLOv8 to address these issues. Firstly, the C2f-DC module is added to the algorithm’s backbone network, enhancing the feature extraction capability of the network. then, the MSCA module is incorporated into the algorithm’s neck network. this module effectively captures multi-scale contextual information, thereby enhancing the feature extraction capability of the algorithm. Finally, the DyHead module and the Inner-IoU loss function are used to replace the original detection head and the original loss function, respectively. the Dyhead module dynamically adjusts feature representations, thus boosting the detection accuracy of the algorithm. the Inner-IoU loss function addresses the issues of poor generalization and slow convergence associated, thereby improving the performance of the algorithm. Experimental results demonstrate that the power line asset detection algorithm designed based on YOLOv8 achieves a mean average precision (mAP) of 90%, accurately detecting power line asset. © 2025 SPIE.

关键词： Power distribution lines

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