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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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Vision Based Currency Counting for An Automated Donation Box

Vision Based Currency Counting for An Automated Donation Box

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Smart Systems for applications in Electrical Sciences (ICSSES), International conference on

作者： K V Jyothi Prakash Hamsini A M Roshni Umesh Mahitha Reddy P Pushpanjali N Department of ECE Siddaganga Institute of Technology Tumakuru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

India is a country renowned as the land of temples serving as a center of spirituality, where people believe in the divine and offer donations as money, jewels, etc. based on their economic status. It is known as ‘kaanike’ and is offered as a symbol of gratitude, devotion, and faith. Almost all temples across the world have a donation box called hundi. These hundis mainly consist of a large number of coins and banknotes. The counting of coins and banknotes requires more human intervention and is a tedious job. Hence, this process of counting coins and banknotes can be automated so that efficiency will be improved and the time consumption of the process will be reduced. In this work we have developed a hundi in which the counting of coins and banknotes will be automated. The system is developed such that the coins and banknotes offered by the devotees will be scanned by the camera, and by using the convolutional neural network (CNN), the denominations of the coins and banknotes are determined. Finally, the cumulative amount is computed by adding the monetary values associated with each coin/banknote.

关键词： image recognition neural networks Symbols Transforms machine learning Organic light emitting diodes Smart systems Feature extraction Convolutional neural networks Currencies

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Can Adversarial Examples be Parsed to Reveal Victim Model Information?

Can Adversarial Examples be Parsed to Reveal Victim Model In...

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Yuguang Yao Jiancheng Liu Yifan Gong Xiaoming Liu Yanzhi Wang Xue Lin Sijia Liu Michigan State University Northeastern University MIT-IBM Watson AI Lab

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Numerous adversarial attack methods have been developed to generate imperceptible image perturbations that cause erroneous predictions in state-of-the-art machine learning (ML) models, particularly deep neural networks (DNNs). Despite extensive research on adversarial examples, limited efforts have been made to explore the hidden characteristics carried by these perturbations. In this study, we investigate the feasibility of deducing information about the victim model (VM)—specifically, characteristics such as architecture type, kernel size, activation function, and weight sparsity—from adversarial examples. We approach this problem as a supervised learning task, where we aim to attribute categories of VM characteristics to individual adversarial examples. To facilitate this, we have assembled a dataset of adversarial attacks spanning seven types, generated from 135 victim models systematically varied across five architecture types, three kernel size configurations, three activation functions, and three levels of weight sparsity. We demonstrate that a supervised model parsing network (MPN) can effectively extract concealed details of the VM from adversarial examples. We also validate the practicality of this approach by evaluating the effects of various factors on parsing performance, such as different input formats and generalization to out-of-distribution cases. Furthermore, we highlight the connection between model parsing and attack transferability by showing how the MPN can uncover VM attributes in transfer attacks.

关键词： Computer vision Perturbation methods Computational modeling Supervised learning Computer architecture machine learning Artificial neural networks Predictive models Data mining Kernel

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Enhancing Object Segmentation via Few-Shot learning with Limited Annotated Data 19th

Enhancing Object Segmentation via Few-Shot Learning with L...

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19th International conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： García-Aguilar, Iván Ali Haider Jafri, Syed Elizondo, David Calderón, Saul Greenfield, Sarah M. Luque-Baena, Rafael ITIS Software University of Málaga C/ Arquitecto Francisco Peñalosa 18 Málaga29010 Spain C/ Doctor Miguel Díaz Recio 28 Málaga29010 Spain De Montfort University The Gateway LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9783031750090

Significant advancements in machine learning in recent years have revolutionized multiple sectors. The Segment-Anything Model (SAM) is a notable example of state-of-the-art image segmentation. Despite claims of zero-shot generalization, SAM exhibits limitations in specific scenarios like medical mammography images. SAM generates three segmentation masks per image to address this and recommends selecting the one with the highest confidence score. However, this is not always the optimal choice. This paper introduces a system that extends SAM’s segmentation capabilities by automatically selecting the correct mask, leveraging few-shot learning methods and an Out-of-Distribution threshold strategy. Several backbones were subjected to experimentation, highlighting the relationship between the support set size and the model’s accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial Intelligence image Segmentation Mammography

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Automated Weed Detection in Agriculture Using Deep learning Approaches: A Comparison of CNN Architectures

Automated Weed Detection in Agriculture Using Deep Learning ...

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International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Zakirul Islam Md. Musfiqur Rahman Mridha Sakib Imtiaz Md. Shahid Ahammed Shakil Md. Fatin Nibbrash Nakib Md. Taufiq Khan Md. Arafat Ibna Mizan Department of Computer Science and Engineering Varendra University Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The foundation of human existence is agriculture. One major problem is the presence of weeds in a crop field. Conventional weed control techniques, such as hand eradication and sporadic pesticide application, are time-consuming and environmentally damaging. This paper suggests an automated weed detection system that uses deep convolutional neural networks (CNNs) and image processing-based methods to classify weeds and crops. Evaluated several CNN architectures with an emphasis on real-time agricultural applications, such as VGG16, MobileNetV2, DenseNet121, EfficientNetB0, InceptionV3, and ResNet50. By adding more deep layers and determining the optimal CNN architecture for the weed detection system, the accuracy is increased during the field test. The best-performing model was VGG16, which obtained a test accuracy of 98.08%, precision of 97.76%, recall of 98.50%, and AUC of 98.18%.

关键词： Deep learning Support vector machines Accuracy Crops Computer architecture Production Real-time systems Convolutional neural networks Residual neural networks Testing

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Enhanced Satellite image Fusion Using Deep learning and Feature Extraction Techniques: A Survey 1st

Enhanced Satellite Image Fusion Using Deep Learning and Feat...

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1st International conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Nallagachu, Swathi Sandanalakshmi, R. Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India

ISBN: (纸本)9783031756047

This paper presents an overview and analysis of numerous research projects on image fusion methods, with a particular emphasis on deep learning-based methods. The research analyses the inadequacies of current fusion models and suggests novel methods for feature extraction, visual sensor networks, remote sensing applications, medical imaging, and multi-resolution image fusion. The study demonstrates the advantages of deep learning techniques for image fusion tasks, including Convolutional neural networks (CNNs), Stacked Auto encoders, and Convolutional Sparse Representation (CSR). These methods provide better fusion quality, fast processing, and better visual perception. Numerous studies provide novel methods, such as the rapid Integer Lifting Wavelet Transform (ILWT), Dual-Tree Complex Contourlet Transform (DT-CCT), and hybrid fusion methods that combine Discrete Cosine Transform (DCT) and Integer Lifting Wavelet Transform (ILWT) techniques. The review literature survey shows how these techniques can improve fusion outcomes while introducing little information distortion. In general, this paper brings together improvements in feature extraction and image fusion techniques, demonstrating the strength and potential of deep learning-based approaches. It is a useful tool for academics and professionals working in various fields who want to comprehend and advance these fields of study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image fusion

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CNN-Based Plant Leaf Disease Identification and Classification for Sustainable Agriculture

CNN-Based Plant Leaf Disease Identification and Classificati...

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Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： A Srilatha P Praveen School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid spread of leaf-diseases among the large-scale crops or plants will pose a great risk to the agricultural output and crop yield. Earlier detection and proper analysis of foliar disorders are essential for minimizing yield losses as well as forwarding continuous farming trials. This research presents a novel way to find and analyze leaf diseases using various deep learning algorithms. The proposed approach integrates Convolutional neural networks (CNN) in feature extraction for automatically identifying disease patterns from leaf-based images with high accuracy to exploit manual inspection of plant leaves. To achieve higher accuracy, this model is train and tested on a wide variety from diseased & healthy leaf images. This approach surpasses traditional image processing methods, exhibiting high precision and recall in identifying various disease types across multiple crops. The deep learning-based solution offers a scalable, efficient, and reliable tool for farmers and agricultural experts, potentially revolutionizing plant disease management strategies. In addition, it can expand the dataset and enhance real-time detection ability which probably could be applied into user-friendly mobile applications in future work

关键词： Deep learning Plant diseases Accuracy Plants (biology) User interfaces Feature extraction Real-time systems Convolutional neural networks Reliability Diseases

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Leveraging AI for Dyslexia Detection: Exploring Multimodal Approaches with machine learning and Deep learning

Leveraging AI for Dyslexia Detection: Exploring Multimodal A...

引用

Sentiment Analysis and Deep learning (ICSADL), International conference on

作者： G.R. Priyasri M. Uma Devi Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Developmental dyslexia is a learning disorder that typically begins in early childhood, making it difficult for affected individuals to read, write, and spell, even when they possess average or above-average intelligence. Children with dyslexia often experience negative emotions such as low selfesteem, frustration, and anger, highlighting the importance of early detection and intervention. Various methods have been proposed for detecting dyslexia, including reading and writing tests, eye tracking, facial image analysis, and advanced imaging techniques such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG). However, one key challenge in dyslexia detection is the reliance on small, unbalanced datasets, which can lead to poor generalization in models. This review suggests that deep learning techniques, particularly those utilizing advanced data augmentation methods, could address this issue by enhancing the model's ability to learn from limited data, improving both accuracy and robustness in real-world applications. The review focuses on the use of machine learning (ML) and Deep learning (DL) for the automatic detection of dyslexia, aiming to improve diagnostic accuracy and accessibility. It explores various ML approaches, such as decision trees and support vector machines, as well as DL architectures, including Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs), which analyze text, speech, and eye-tracking patterns. The review provides a comparison of feature extraction methods, classification techniques, and performance metrics, with an emphasis on data enhancement and interpretability. Additionally, it addresses challenges such as dataset variability, ethical concerns, and the practical application of these methods in real-world settings. The goal of this review is to provide an overview of the most current methods for dyslexia detection and propose innovative ways to incorporate ML and DL into personalized learning plans an

关键词： Deep learning Support vector machines Sentiment analysis Accuracy Reviews Magnetic resonance imaging Gaze tracking Dyslexia Brain modeling Electroencephalography

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on image processing, Computer Vision, and Pattern Recognition. The topics include: image-Based Seal Recognition: Approaches and Challenges in Current Automated Systems;Deep learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light image Enhancement Using Autoencoder-Based Deep neural networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view applications in Hand-Drawn Sketch images;residential Real Estate image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color images;lalitha: A Hand Gesture-Based Computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional neural networks and image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern Technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain Technology in the Transmission of Semiconductor Process Recipes;the Use of Social Media and the Internet for the Facilitation of Human Trafficking.

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Advanced Algorithms to Detect and Prevent the Spread of Manipulated images and Videos Using Deep learning and AI

Advanced Algorithms to Detect and Prevent the Spread of Mani...

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5th International conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2024

作者： Pachimatla, Divya Kilari, Rampriya Ranitha, I.B. Ranaveer, Indur Srinivasa Rao, Katakam Renuka, Kummari CMR Institute of Technology Telangana Hyderabad 501401 India

ISBN: (纸本)9789819788644

Advanced strategies for identification and prevention are required because of the major challenge posed by the growth of modified photos and videos in digital media. To solve this problem, this study investigates the use of artificial intelligence (AI) and deep learning. One of the study’s main goals is to create and apply advanced algorithms that can recognise modified visual material. The paper also emphasises the significance of variety in datasets and the continuous difficulties in obtaining real-time detection skills. The ethical issues surrounding the creation and application of such technologies are explored, as well as techniques for preventing adversarial assaults on detection models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Cybersecurity Deep learning image manipulation neural networks

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AI Based Targeting and Tracking System

AI Based Targeting and Tracking System

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Computational, Communication and Information Technology (ICCCIT), International conference on

作者： Megha Kolhekar Pratik Mhatre Omkar Yadav Arkam Khan Avantika Pardeshi Department of Electronics & Telecommunication Engineering Agnel Charities' Fr. C. Rodrigues Institute of Technology Navi Mumbai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

This paper introduces an AI-driven tracking and targeting system poised to revolutionize precision across surveil- lance, defense, and tracking. Traditional systems often struggle with dynamic environments and accuracy optimization. Lever- aging AI, our system integrates advanced computer vision and deep neural networks for real-time object detection and tracking. Through machine learning, the system adapts to evolving scenarios, progressively enhancing tracking performance. Additionally, prevention of casualties with respect of loss of life, this model acts as a firewall from possible threats. This AI-based system signifies a significant advancement, promising heightened precision and efficiency across diverse applications. Its integration of cutting- edge technologies positions it at the forefront of tracking and targeting systems, marking a noteworthy contribution to the field.

关键词： Computer vision Target tracking Firewalls (computing) Prevention and mitigation image edge detection Object detection machine learning Real-time systems Information and communication technology Optimization

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