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2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Maheswari, S. Bhuvaneshwaran, A. Rajalakshmi, J. Nivetha, S.B. Kishore, R. Deepanjali, S. Department of Biomedical Engineering Velalar College of Engineering and Technology Erode India

ISBN: (纸本)9798350340235

This study focuses on using medical imaging and machine learning to address Ectopic pregnancies (EPs) during the first trimester. The variability in ultrasound image quality and the presence of noise can hinder accurate image analysis in case of manual review by physicians, thus, this study aims to develop an automated system for fetal ultrasound image analysis. The research employs advanced image processing methods, including Region of Interest (ROI) selection and image enhancement techniques. Three classification algorithms CNN, SVM, and KNN are compared to select the most suitable one based on performance metrics. This suggests that CNN is a promising tool with 97% accuracy for the early detection of EP. The proposed system has the potential to transform the diagnosis and management of EP, potentially improving maternal outcomes. The goal is to enhance the precision and effectiveness diagnosis, potentially transforming early-stage pregnancy diagnostics. © 2023 IEEE.

关键词： Diagnostic accuracy Early diagnosis Ectopic pregnancy image enhancement image processing Machine learning Maternal-fetal health Medical imaging Ultrasound images

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Intelligent Video Surveillance System with Abnormal Behavior Recognition and Metadata Retrieval

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IEIE Transactions on Smart processing and Computing 2024年第6期13卷 541-552页

作者： Kim, Hyungtae Shin, Joongchol Park, Seokmok Paik, Joonki Department of Image Chung-Ang University Seoul06974 Korea Republic of Department of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of

Huge-scale video surveillance systems have become essential in crime prevention and situation recording. Traditional surveillance systems relied on human monitoring of video streams, which often led to errors and difficulties in understanding events. Furthermore, locating specific scenes within recorded videos required extensive human investigation. To overcome these inefficiency, inconvenience, and potential risk challenges, we propose an intelligent analysis scheme that utilizes abnormal behavior recognition and metadata retrieval algorithms to replace human monitoring. The proposed method consists of three stages: i) basic metadata generation through object detection and tracking, ii) abnormal behavior recognition for event metadata, and iii) SQL-based metadata retrieval. By incorporating specific information such as object color and aspect ratio, our technique enhances retrieval usability. Moreover, our module for recognizing abnormal behavior demonstrates robust classification capabilities for activities such as pushing, violence, falling, and crossing barriers. Since our system considers the possible harsh scenarios that cause the computation limitation of edge devices, we choose the adoptable best algorithm for each edge device. In addition, analysis sever complements the detection results of edge cameras. As a result, the proposed method can robustly generate the metadata without any object exception and search the specific object by query. Therefore, the proposed method can be seamlessly deployed on both edge cameras and analysis servers, making it adaptable to various surveillance setups. This approach revolutionizes the traditional surveillance paradigm, enabling more efficient, reliable, and secure video monitoring and analysis. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Security systems

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Intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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Object detection algorithm based on residual deformable convolution pyramid 4

Object detection algorithm based on residual deformable conv...

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4th International conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Huang, Bin Wang, Sanqian Li, Linjie Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang*** China School of Software Nanchang Hangkong University Nanchang*** China

ISBN: (纸本)9781510682528

Considering the challenges associated with robots in optoelectronic imaging applications, typically require real-time and accurate recognition and localization of targets, especially in complex environments. Due to the potential challenges of noise, blurring, or dense targets in the images obtained by optoelectronic imaging systems, traditional object detection algorithms may face issues such as semantic information loss and difficulty in detecting small targets. By introducing residual deformable convolution structures, feature representation can be enhanced in optoelectronic imaging systems, and the shape of the convolution kernel can be dynamically adjusted to adapt to the non rigid deformation of the target, thereby better capturing the detailed features of small targets. Experimental results exhibit that RAFPN enhances the mAP of object detection algorithms RetinaNet, Faster R-CNN, and GFL by 1.2%, 0.9%, and 1.1% respectively on the MS COCO dataset, effectively enhancing the performance of object detection algorithms in optoelectronic imaging. © 2024 SPIE.

关键词： Optoelectronic devices

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image Recognition and Analysis of Computer Interlock Interface Based on OpenCV Technology 6

Image Recognition and Analysis of Computer Interlock Interfa...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2024

作者： Chen, Xuanmo Li, Qiang Lanzhoujiaotong University College of Computer Science and Technology Lanzhou China Ministry of Education Province Key Laboratory of Optoelectronic Technology and Intelligent Control Lanzhou China

ISBN: (纸本)9798350316520

An integrated computerized locking mechanism is vital for upholding the safety of train movements within railway networks. As computer technology progresses swiftly, conventional locking systems are encountering a multitude of difficulties, with the identification and processing of visual interface data being a key challenge. Furthermore, the pace of technological innovation has led to an array of other obstacles for these systems to surmount. The deployment of cutting-edge technologies, including AI and machine learning, has markedly enhanced the functionality and security of these mechanisms. Anticipatedly, the future will witness the development of even more sophisticated and secure railway traffic management systems. These evolving technologies serve as formidable instruments in tackling the prevailing issues and challenges. Specifically, the computerized interface locking system is indispensable in guaranteeing the safe running of trains within railway *** paper applies the image processing capability of OpenCV to preprocess and extract characteristics from the interlocking interface image. Subsequently, the enhanced clustering algorithm of RV-DBSCAN is utilized to identify the text of signaling equipment diagrams, followed by the implementation of the Paddle OCR (Optical Character Recognition) machine learning algorithm to classify and identify the images. The recognition accuracy rate for information related to railway signaling equipment stands at 92.6%. This process offers a dependable approach for extracting and recognizing station information from computerized interlocking interfaces. © 2024 IEEE.

关键词： Clustering algorithms

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MicroDeblur: image Motion Deblurring on Microcontroller-based Vision systems 22

MicroDeblur: Image Motion Deblurring on Microcontroller-base...

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22nd ACM/IEEE International conference on Information processing in Sensor Networks (IPSN)

作者： Lee, Seulki UNIST Ulsan Natl Inst Sci & Technol Ulsan South Korea

ISBN: (纸本)9798400701184

This paper introduces MicroDeblur, an on-device image motion deblur solution for resource-constrained microcontroller-based vision systems. Although motion blurs caused by the movement or shake of the device (camera) are pervasive in embedded, IoT, and mobile devices, it has been considered a hard nut to crack for many microcontrollers with extremely-limited resources (e.g., hundreds of KB of RAM). To tackle this problem, we combine the DNN (deep neural network) motion deblur method with the classical motion deblur approach and take the best of both worlds, i.e., 1) powerful pattern recognition ability of DNNs and 2) simplicity and stability of matrix-based classical algorithms. To deblur an image, MicroDeblur takes three steps: 1) blur kernel estimation, 2) blur image transformation, and 3) iterative clear image restoration. We propose 1) depth-independent convolution that efficiently estimates the blur kernel (pattern) and 2) Toeplitz-based motion blur modeling that enhances the time and space complexity of the deblurring process by O(n) and O(n(3)), respectively, compared to the existing methods. To the best of our knowledge, MicroDeblur is the first self-sufficient blind deconvolution solution for a stand-alone microcontroller that does not rely on extra hardware or external systems. We implement MicroDeblur on an ARM Cortex-M4F, achieving a competitive quality of deblurred images using 187x and 429x smaller memory and energy, respectively, compared to high-end GPU-based solutions.

关键词： image deblurring blind deconvolution on-device deep neural networks microcontroller

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Precision Pistachios: Enhancing Agricultural Quality Control with Deep Learning 2

Precision Pistachios: Enhancing Agricultural Quality Control...

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2nd International conference on Intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Kaur, Arpanpreet Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Originating from the pistachio tree (Pistacia vera) and prized for their nutritional content and adaptability in the kitchen, pistachios have great financial worth in the agricultural field. Ensuring the quality and safety of pistachio nuts is absolutely vital and calls for effective industrial post-harvest methods. Modern technology, especially image processing and computer vision algorithms, are augmenting conventional approaches of pistachio categorization and separation. This work intends to build an enhanced classification model for pistachio identification using deep learning, most especially the VGG16 Convolutional Neural Network (CNN). After gathering a dataset of 2,148 high-resolution photos of pistachios, the approach uses resizing and data augmentation methods including rotation, flipping, and zooming to improve the generalization and prevent overfitting of the model. Pretrained on imageNet, the VGG16 model is fine-tuned to fit the particular job of pistachio recognition by means of feature extraction capacity. For feature extraction, the model architecture comprises convolutional layers;for classification, it features fully connected layers. With performance criteria including accuracy, precision, recall, and F1-score watched to guarantee efficient learning, optimization methods including gradient descent and backpropagation are utilized during training. On a test set, the model shows an extraordinary accuracy of 0.98, so highlighting the potential of deep learning models-especially VGG16-in automating and improving quality control systems in agricultural uses. With possible uses in related agricultural research, this highperformance classification model not only meets the demand for effective separation of pistachio species but also considerably increases their economic value.

关键词： Pistachio detection Convolutional Neural Networks (CNNs) VGG16 image processing Computer vision Deep learning Classification model Data augmentation Quality control Agricultural Feature extraction post-harvest processes Economic species

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Various Approaches in Deep Learning for Medical Modality image Segmentation 1

Various Approaches in Deep Learning for Medical Modality Ima...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Lavanya, K. Vijay, Devi P

ISBN: (纸本)9798350348057

In computer vision and image processing the surveillance of Video, Virtual reality, robotic perception, and image compression are just a few of the uses for image segmentation. There are a plethora of segmentation algorithms out there right now. These new approaches to picture segmentation have been spurred on by the extensive victory of Deep Learning (DL). Semantic and instance segmentation literature is thoroughly reviewed, with particular attention paid to groundbreaking work in CNN models, architecture of encoder-decoder, recurrent networks, attention models, and GAN settings. This work is a comprehensive review of the most recent research in these fields. Using a wide range of datasets, we assess the performance of different deep learning-based segmentation representation, examine their strengths and drawbacks, and identify promising future avenues © 2023 IEEE.

关键词： Semantics

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Fuzzy Net for image processing Applications: image Segmentation 6th

Fuzzy Net for Image Processing Applications: Image Segmentat...

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6th International conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Alameen, Huda A. Kareem, Noor Riyadh Habeeb, Sundus Q. Department of Computer Science Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq Faculty of Medicine Jabir Ibn Hayyan Medical University Najaf Iraq

ISBN: (纸本)9789811995118

Since the invention of computers, image processing methods have been utilized in a variety of applications, where their significance has grown. An important topic and the main emphasis of image processing methods are image segmentation, which is also a classic topic in the area. In order to segment images, a number of general-purpose algorithms and methods have been created. Since the image segmentation problem does not have a generic solution, these strategies frequently need to be paired with domain expertise to successfully solve an image segmentation problem for a problem domain. In this paper, we present a comparative study of basic two types of fuzzy convergence with a midpoint as image segmentation techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Research on image Edge Detection Method Based on Wolf King Algorithm 7

Research on Image Edge Detection Method Based on Wolf King A...

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7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Jian, Chang Yimeng, Fan School of Software Liaoning Technical University Huludao125105 China

ISBN: (纸本)9798350350760

Aiming at the problem that the current image edge detection algorithm is not accurate enough to capture the edge contour, an image edge detection algorithm based on Wolf king algorithm was proposed. Firstly, local principal component analysis and generalized Gaussian prior were used to improve the Gaussian filter, so that the image noise was effectively suppressed and the detail information was significantly enhanced. Secondly, multi-direction gradient calculation is proposed to reduce the influence of noise on gradient estimation and provide more information to locate the edge, so as to determine the position and direction of the edge more accurately. Finally, the Wolf king algorithm proposed in this paper is based on the grey Wolf algorithm, which is improved by using the nonlinear convergence factor and adaptive weight strategy. The improved algorithm is used to optimize Otsu to obtain the image threshold, realize the automatic acquisition of the image high and low threshold, and further improve the accuracy of the algorithm. More detailed edge contour information can be extracted from the image processed by the above method. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the images processed by the LPG-PCA image denoising algorithm are increased by 18.8% and 13.6% respectively compared with the traditional Gaussian filtering. In terms of threshold processing, compared with most existing edge detection algorithms, the Wolf king algorithm proposed in this paper can accurately detect the edge contour, and it is more coherent in edge extraction, with better unilateral response effect and better algorithm performance. ©2024 IEEE.

关键词： Wiener filtering

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