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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

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image processing based Scoring System for Small Arms Firing in the Military Domain 22

Image Processing based Scoring System for Small Arms Firing ...

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4th international conference on image processing and Machine vision, IPMV 2022

作者： Chandan, Md Rezoanul Hafiz Naim, Tanvir Ahamad Razzak, Md Abdur Sharmin, Nusrat Department Of Computer Science And Engineering Military Institute Of Science And Technology Bangladesh

ISBN: (纸本)9781450395823

Firing and its overall conduct of scoring plays a very vital role in the military organizations. Currently, the evaluation of such a system is partially automated that still needs manual works. A fully automatic calculation of target scoring is time demanding and can add a great value in minimizing human error. this paper puts focus on designing and developing the process of scoring a fired target automatically based on image processing techniques in the context of military domain. Along with conventional image processing techniques, this work has used structural similarity indexing algorithms to detect the bullet holes from images. the experiment has been conducted on real-time scenarios considering the military field and shows a promising result to calculate the scores automatically. © 2022 ACM.

关键词： Indexing (of information)

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Ocular Eye Disease Prediction Using Machine Learning 15th

Ocular Eye Disease Prediction Using Machine Learning

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kongala, Lavanya Bikku, thulasi Preethi, B. Zaveriya Ruman, S.K. Susmitha, P. Sowjanya, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Vengal522503 India Department of Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Guntur India

ISBN: (纸本)9783031810794

the human eye, often considered the most crucial sense organ for perceiving and understanding the world, plays a fundamental role in our ability to see and interpret our surroundings. However, vision can be significantly impacted by various ocular eye disorders, which pose significant challenges to our visual capabilities. Among these disorders, one of the most prevalent conditions is cataract, a condition characterized by cloudiness in the lens of the eye, resulting in impaired vision and a blurred perception of objects. Typically, cataracts are more commonly found in individuals of advanced age due to natural aging processes. Identifying and diagnosing ocular eye illnesses can be a complex and challenging task, often requiring sophisticated techniques and tools to aid in the process. In recent research endeavors, automated learning algorithms have emerged as valuable tools for assisting in the detection of ocular eye disorders. these algorithms, such as Convolutional Neural Networks (CNNs), are employed alongside image pre-processing techniques to analyze and interpret ocular data. By leveraging the power of machine learning, researchers have been able to develop and refine predictive models for ocular diseases. To evaluate the accuracy of disease prediction models, various design pattern graphs can be utilized. these graphs serve as visual representations, showcasing the performance and reliability of the algorithms in predicting ocular eye disorders. By employing these advanced technological approaches, researchers and medical professionals can enhance their ability to identify and diagnose ocular conditions, ultimately leading to more effective treatments and interventions for individuals affected by these diseases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence 4th

Fish Blood Cell as Biological Dosimeter: In Between Measurem...

引用

4th international conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Felicetti, Francesco Carnì, Domenico Luca Lamonaca, Francesco Department of Computer Engineering Modeling Electronics and Systems Engineering University of Calabria Ponte P.Bucci Arcavacata Di Rende87036 Italy CNR-NANOTEC National Research Council Rende Italy

ISBN: (纸本)9789819769919

Analyzing fish cell dimensions holds significant importance in biological research due to its implications for understanding fish physiology, health, and environmental interactions. this study poses a preliminary insight on the relevance of analyzing fish cell dimensions, particularly blood cells, for assessing fish health and monitoring environmental pollution. Alterations in cell size, shape, or proliferation rates can indicate physiological stress, disease conditions, or responses to environmental factors. the new image Flow Cytometer has allowed the acquisition of thousands radiomic images of blood cell per unit of time. Advanced measurement techniques, including improved algorithms, machine learning, and convolutional neural networks (CNNs), have revolutionized morphometric analysis by enabling precise and efficient extraction of cellular measurements. Additionally, big data analytics offers opportunities to enhance cell dimension analysis by providing vast datasets for robust feature extraction and pattern recognition. Addressing measurement uncertainty and establishing ground truth are crucial for ensuring data reliability and validity. Integrating environmental and species-specific parameters into big data analyses enables comprehensive risk assessment for fish consumption. this multidisciplinary approach facilitates informed decision-making regarding fish health monitoring and environmental management strategies, ultimately contributing to human well-being and ecosystem conservation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Environmental monitoring

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Research on UAV target detection and substation equipment status recognition technology based on computer vision 4

Research on UAV target detection and substation equipment st...

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2022 4th international conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Yi, Liu Xin, Yuchen Chen, Zuodong Lin, Jianwei Liu, Xiaowei Fujian362000 China State Grid Fujian Electric Power Research Institute 2 Fuzhou Fujian362000 China Fujian362000 China Wuhan Aoruier Technology Co. Ltd. China Wuhan Aoruier Technology Co. Ltd. Hubei430000 China

the use of computer vision-related technologies can more efficiently achieve target detection of UAVs and accurately identify the status of substation equipment. therefore, this paper proposes research on UAV target detection and substation equipment status recognition technology based on computer vision. Taking key technical links such as image preprocessing based on computer vision technology, image feature extraction, and construction of UAV target detection coordinate system as breakthrough points, realizing UAV target detection work lays a solid foundation for the identification of substation equipment status. It lays the foundation and promotes the scientific and effective identification of the status of substation equipment. It provides a guarantee for improving the accuracy of UAV target detection and promoting the efficient and stable identification of substation equipment status. CLC number: TN919.8 Document identification code: A. © Published under licence by IOP Publishing Ltd.

关键词： Unmanned aerial vehicles (UAV)

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

4th International Symposium on Intelligent Computing Systems...

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

ISBN: (纸本)9783030984564

the proceedings contain 11 papers. the special focus in this conference is on Intelligent Computing Systems. the topics include: On the Performance of Preconditioned Methods to Solve Lp -Norm Phase Unwrapping;Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption;DDoS Attack Preventing and Detection with the Artificial Intelligence Approach;iris recognition Using Supervised Learning Based on Matching Features;convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay;Evaluation of Human SCD Test by Digital image Analysis;machine vision-Based Expert System for Automated Skin Cancer Detection;AI Used to Identify Car Accident Injury and Mortality Risk Factors in Dubai UAE;toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring through pattern Classification of Water Crystallization.

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A Novel Transformer-Based Approach to Low-Light image Enhancement

A Novel Transformer-Based Approach to Low-Light Image Enhanc...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Guodong Xia Yongde Guo Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

image quality significantly impacts the effectiveness of computer vision (CV) applications such as autonomous driving, facial recognition, and remote sensing. However, inadequate lighting substantially degrades image quality, resulting in muted colors, reduced visibility, distortion, increased noise, and loss of detail, which can significantly interfere with the performance of these CV-based applications. In response, our research introduces the Fusion Low-Light Former (FLLF) to address the critical need for enhancing images captured in low-light conditions. Our method uses transformer-based techniques and feature fusion to effectively merge low-and-high-level features in degraded images in a hierarchical architecture. Consequently, our FLLF markedly improves low-light image enhancement performance.

关键词： image quality computer vision image color analysis Face recognition Lighting computer architecture Transformers

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Teaching and Learning based Optimization with Deep learning Model for Diabetic Retinopathy Grading and Classification 4

Teaching and Learning based Optimization with Deep learning ...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Ramesh, R. Sathiamoorthy, S. Annamalai University Department of Computer and Information Science Annamalai Nagar India Annamalai University Pg Extension Centre Tamilnadu Villupuram India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Diabetic retinopathy (DR) refers to a disease that leads to diabetes complications, causing non-reversible damage to retina blood vessels. DR becomes an important reason for impaired vision if not identified initially. the manual analysis procedure of DR retina fundus image with ophthalmologists consumes more time and is expensive and also has the chance of misdiagnosis different computer-aided diagnosis (CAD) techniques. In recent times, deep learning (DL) turns out to be most common method that has reached superior performance in several regions, particularly in clinical image analysis and classification. this study has presented the fully automatic analysis models which surpass manual approaches for avoiding misdiagnosis, minimizes time, cost, and effort. A novel Teaching and Learning based Optimization with Deep learning for Diabetic Retinopathy Grading and Classification (TLBODL-DRGC) model is introduced. the presented TLBODL-DRGC model inspects the retinal fundus image for the recognition and classification of different DR stages. In the TLBODL-DRGC model, the Gabor filtering (GF) system was applied for preprocessing the retinal image, and Otsu thersholding is employed to segment them. Next, the features are developed in them by the use of EfficientNetB0 method, and TLBO based hyperparameter optimizer is presented to enhance the classifier results. Finally, extra-tree classifier (ETC) model is exploited to properly determine the different stages of DR. the presented TLBODL-DRGC model is evaluated using benchmark retinal image database and the results demonstrate the promising performance over other DL models. © 2022 IEEE.

关键词： computer vision

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Analysis of the effectiveness of object recognition algorithms using a thermal imaging camera

Analysis of the effectiveness of object recognition algorith...

引用

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Jaroslaw Kozik Aleksander M. Nawrat Krzysztof Daniec Department of Automatic Control and Robotics Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

the work presents a comparison of the object recognition methods based on three algorithms (Eigenface, Fisherfaces, LBPH - Local Binary patterns Histograms), which were extended to include thermal imaging. the aim of the work is to select the most effective analytical method in changing environmental conditions and implement it in the form of software for a technological project called OctoView. the system placed on a drone, combined with efficient processing units and Jetson NANO microcomputers in cooperation with a multi-vision system, will provide comprehensive analysis and image assessment of “threats” located in the observed area, recording field events, tactical risk analysis and reports, monitoring areas border areas, status and position and type of own resources in border (or other) sections. this project is dedicated to military, border, customs, fire brigade and police applications. the purpose of implementing the software is to increase security in border areas through continuous image observation and analysis [1]–[3], as well as monitoring threats and their dynamics.

关键词： image recognition Heuristic algorithms Software algorithms thermal engineering Cameras Software thermal analysis Object recognition Security Monitoring

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Fake Face image Classification by Blending the Scalable Convolution Network and Hierarchical vision Transformer 1

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4th international conference on computer and Communication Technologies, IC3T 2022

作者： Kerenalli, Sudarshana Yendapalli, Vamsidhar Mylarareddy, C. Department of CSE SoT GITAM University Karnataka Bengaluru562163 India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

A face has been used as a primary and unique attribute to authenticate individual users in emerging security approaches. Cybercriminals use the double-edged sword "image processing" capabilities to deceive innocent users. the underlying technology is based on advanced machine learning and deep learning algorithms. the intentions of cyber criminals range from simple mimicking or trolling to creating violent situations in society. Hence, it is necessary to resolve such problems by identifying the fake face images generated by expert humans or artificial intelligent algorithms. Machine learning and artificial neural networks are used to resolve the issue. In this work, we have designed an approach for detecting deep learning-generated fake face images by combining the capabilities of the scalable convolutional neural networks (CNN) "EfficientNet" and hierarchical vision transformer (ViT) "shifted window transformer". the proposed method accurately classifies the fake face images with a 98.04% accuracy and a validation loss of 0.1656 on the 140 k_real_fake_faces image dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Handwritten Character recognition from image Using CNN 1

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

作者： Chakraborty, Partha Roy, Shanta Sumaiya, Sadia Nowshin Sarker, Aditi Department of Computer Science and Engineering Comilla University Cumilla3506 Bangladesh

ISBN: (数字)9789811995125

ISBN: (纸本)9789811995118

Handwritten character identification has always been an intriguing area of study in the realm of pattern recognition in image processing. Because of its numerous applications, the requirement for identifying handwritten characters is growing every day. Many scholars have defined their work in this field, and additional research is being carried out to obtain high precision. In compared to other major languages such as Bangla, there are numerous works in handwritten character recognition available for English. the goal is to present a comprehensive, effective, and efficient method for classifying and recognizing both Bangla and English letters. An extended convolution neural network (CNN) model has been suggested to recognize Bangla and English characters. Character recognition is achieved through segmentation, feature extraction, and classification. CNNs were recently discovered to be adept at English text detection. A CNN-based Bangla handwritten character recognition system is also being researched. A total of 23,040-character samples have been used, with 25% of the data having served as a test set and the remaining 75% having been used to train the recognition model. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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