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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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Provable Editing of Deep neural networks using Parametric Linear Relaxation 38

Provable Editing of Deep Neural Networks using Parametric Li...

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38th conference on neural Information processing Systems, NeurIPS 2024

作者： Tao, Zhe Thakur, Aditya V. University of California Davis DavisCA95616 United States

Ensuring that a DNN satisfies a desired property is critical when deploying DNNs in safety-critical applications. There are efficient methods that can verify whether a DNN satisfies a property, as seen in the annual DNN verification competition (VNN-COMP). However, the problem of provably editing a DNN to satisfy a property remains challenging. We present PREPARED,1 the first efficient technique for provable editing of DNNs. Given a DNN N with parameters θ, input polytope P, and output polytope Q, PREPARED finds new parameters θ such that ∀x ∈ P. N(x;θ) ∈ Q while minimizing the changes θ − θ. Given a DNN and a property it violates from the VNN-COMP benchmarks, PREPARED is able to provably edit the DNN to satisfy this property within 45 seconds. PREPARED is efficient because it relaxes the NP-hard provable editing problem to solving a linear program. The key contribution is the novel notion of Parametric Linear Relaxation, which enables PREPARED to construct tight output bounds of the DNN that are parameterized by the new parameters θ. We demonstrate that PREPARED is more efficient and effective compared to prior DNN editing approaches i) using the VNN-COMP benchmarks, ii) by editing CIFAR10 and TINYimageNET image-recognition DNNs, and BERT sentiment-classification DNNs for local robustness, and iii) by training a DNN to model a geodynamics process and satisfy physics constraints. © 2024 neural information processing systems foundation. All rights reserved.

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4th International conference on artificial Intelligence in China, AIC 2022

4th International Conference on Artificial Intelligence in C...

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4th International conference on artificial Intelligence in China, AIC 2022

ISBN: (纸本)9789819912551

The proceedings contain 49 papers. The special focus in this conference is on artificial Intelligence in China. The topics include: Research on Helmet Detection Algorithm Based on Improved YOLOv5s;label Embedding Based Scoring Method for Secondary School Essays;Research on Wheat Ears Detection Method Based on Improved YOLOv5;Aircraft Target Detection Algorithm Based on Improved YOLOv5s;a Survey in Virtual image Generation Based on Generative Adversarial networks;research on the Service for the Disabled in Interior Design and Product Design Based on artificial Intelligence;application and Prospect of artificial Intelligence image Analysis Technology in Natural Resources Survey;application and Technical Analysis of Computer Vision Technology in Natural Resource Survey;research on Natural Language processing Technology in Construction Project Management;review of Generative Adversarial networks in Object Detection;a Review of the Application of Convolutional neural networks in Object Detection;optimal Deployment of Large-Scale Wireless Sensor Network Based on Topological Potential Adaptive Graph Clustering;INSL: Text2SQL Generation Based on Inverse Normalized Schema Linking;the Research on Prediction for Financial Distress in Car Company Listed Combining Financial Indicators and Text Data;frame Interpolation for Weather Radar Data;a Hybrid neural Network for Music Generation Using Frequency Domain Data;Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: The Case of China;certifiable Optimal Visual Pose Estimation for Space applications;Automotive Manufacturing Revenue Prediction Using Financial and Comment Sentiment Data Based on CNN Model;exploring the Application of SketchUp and Twinmotion in Building Design Planning;Deep Learning-Enhanced ICA Algorithm for Sub-Gaussian Blind Source Separation.

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A Comparison of Convolutional neural Network Models for Driver Fatigue Detection 7

A Comparison of Convolutional Neural Network Models for Driv...

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7th International conference on image and Signal processing and their applications, ISPA 2022

作者： Nedjar, Imane Sekkil, Hicham Mohamed Mebrouki, Mahmoud Bekkaoui, Mokhtaria Higher School of Applied Sciences of Tlemcen Biomedical Engineering Laboratory Tlemcen Algeria Higher School of Applied Sciences Second Cycle Department Tlemcen Algeria Tlemcen Algeria

ISBN: (纸本)9781665480420

Road traffic crashes are among the significant risks facing millions of people around the world every day. Driver fatigue is a salient factor in road accidents. However, overcoming this factor has become possible with the use of artificial intelligence. In fact, with the development of technology, industrial companies in the automotive sector are working on intelligent cars capable of identifying the risks and avoiding them. In this work, we propose a method that identifies driver fatigue. First, we established a comparison between 10 models of Convolutional neural networks (CNNs), to classify the state of both mouth and eyes. After selecting the best of them, we compute the percentage of eye closure (PERCLOS) and yawning frequency of mouth (FOM) to conclude the driver state. The proposed method obtained an accuracy of 87.5%. © 2022 IEEE.

关键词： Convolutional neural networks

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GM-GAN: Geometric Generative Models Based on Morphological Equivariant PDEs and GANs 27th

GM-GAN: Geometric Generative Models Based on Morphological E...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Diop, El Hadji S. Fall, Thierno Mbengue, Alioune Daoudi, Mohamed Department of Mathematics NAGIP-Nonlinear Analysis and Geometric Information Processing Group University Iba Der Thiam BP 967 Thies Senegal Department of Mathematics and Computer Science University Cheikh Anta Diop Dakar Senegal IMT Nord Europe Univ. Lille Centre for Digital Systems Lille59000 France Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL Lille59000 France

ISBN: (纸本)9783031783883

This work deals with image generation, two main problems are addressed: (i) improvements of specific feature extraction while accounting at multiscale levels intrinsic geometric features, and (ii) equivariance of the network for reducing the complexity and providing a geometric interpretability. We propose a geometric generative model based on an equivariant partial differential equation (PDE) for group convolution neural networks (G-CNNs), so called PDE-G-CNNs, built on morphology operators and generative adversarial networks (GANs). The proposed geometric morphological GAN model, termed as GM-GAN, is obtained thanks to morphological equivariant convolutions in PDE-G-CNNs. GM-GAN is evaluated qualitatively and quantitatively using FID on MNIST and RotoMNIST, preliminary results show noticeable improvements compared classical GAN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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International conference on Emerging Trends and applications in artificial Intelligence, ICETAI 2023

International Conference on Emerging Trends and Applications...

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International conference on Emerging Trends and applications in artificial Intelligence, ICETAI 2023

ISBN: (纸本)9783031567278

The proceedings contain 47 papers. The special focus in this conference is on Emerging Trends and applications in artificial Intelligence. The topics include: artificial neural Network Model of Nonlinear Behavior of Micro-ring Gyroscopes;a Framework for Knowledge Representation Integrated with Dynamic Network Analysis;time Series Forecasting Using Parallel Randomized Fuzzy Cognitive Maps and Reservoir Computing;review of Offensive Language Detection on Social Media: Current Trends and Opportunities;Text Mining and Sentimental Analysis to Distinguish Systems Thinkers at Various Levels: A Case Study of COVID-19;ADHD Prediction in Children Through Machine Learning Algorithms;commonsense Validation and Explanation for Arabic Sentences;predicting Students Answers Using Data Science: An Experimental Study with Machine Learning;arabic News Articles Classification Using Different Word Embeddings;tree Fruit Load Calculation with image processing Techniques;prediction and Analysis of Water Quality Using Machine Learning Techniques;comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm;supermarket Shopping with the Help of Deep Learning;A Decision Support System for Detecting FIP Disease in Cats Based on Machine Learning Methods;a Numerical Simulation for the Ankle Foot Orthosis Using the Finite Element Technique with the Aid of an Experimental Program;numerical and Experimental Simulations of Damage Identification in Carbon/Kevlar Hybrid Fiber-Reinforced Polymer Plates Using the Free Vibration Measurements;computer Modelling of the Gait Cycle Patterns for a Drop Foot Patient for the Composite a Polypropylene Ankle-Foot Orthoses;arabic Sign Language Alphabet Classification via Transfer Learning;evaluation of Chemical Data by Clustering Techniques;novel Quantum Key Distribution Method Based on Blockchain Technology;smart Parking System Based on Dynamic and Optimal Resource Allocation;spatio-Angular Resolution Trade-Off in Face

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Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction 2

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Yogaraja, C.A. Priyanka, C. Priyadharshini, S.P. Babu, J. Jagan Rajagopal, S. Poorani, S. Ramco Institute of Technology Rajapalayam India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus India St. Joseph's College of Engineering OMR Chennai119 India Department of ECE R.M.D Engineering College Kavaraipettai India Department of IT National Engineering College Kovilppati India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350383867

Insect image recognition (iiR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. iiR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (iiR-SCSODFR) model. The developed iiR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed iiR-SCSODFR model signifies an important leap forward in iiR, and provides a forceful and precise solution for automated identification of various insect species. © 2024 IEEE.

关键词： Convolutional neural networks

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Aircraft Recognition Using Convolutional neural Network

Aircraft Recognition Using Convolutional Neural Network

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2023 International conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Wan Zulkipli, Nabilah Chuah, Joong Huang Lim, Marcus Guozong Chai, Grace Mei Ting Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur52200 Malaysia Institute for Manufacturing Department of Engineering University of Cambridge CambridgeCB3 0FS United Kingdom Faculty of Engineering and Physical Sciences University of Southampton Malaysia Johor Iskandar Puteri79200 Malaysia

ISBN: (纸本)9781839539596

Satellite image object-recognition offers detailed location representation where ground-based sampling is limited or inaccessible, such as high sea or in the mountains. However, an accurate and effective method is required to ensure the precision of the information generated due to small, ambiguous objects against the background. Having an automatic object detection system would greatly speed up satellite image recognition and find extensive applications in defense, transportation, agriculture, and emergency relief. With the advancement of artificial neural networks, the technology has seen applications in various fields from image processing to speech synthesis. We aimed to develop a neural network-based commercial aircraft detection system on satellite images at major airports. We proposed a R-CNN-based aircraft recognition system where CNN is utilized to extract features of aircraft before passing the feature maps to the binary classifier. We trained the model with 2100 images of satellite images containing aircraft which are segmented to produce positive and negative instances and validated on 900 images. The system is equipped to detect and localize the position of the aircraft in an image while registering excellent accuracy. However, as the number of aircraft in a frame increases, slight deterioration in the model performance is observed. © The Institution of Engineering & Technology 2023.

关键词： Object detection

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Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks 57

Synaptic turnover promotes efficient learning in bio-realist...

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57th Asilomar conference on Signals, Systems and Computers

作者： Malakasis, Nikos Chavlis, Spyridon Poirazi, Panayiota UoC Sch Med Iraklion Greece FORTH IMBB Iraklion Greece Tech Univ Munich Sch Life Sci Weihenstephan Munich Germany

ISBN: (纸本)9798350325744

While artificial machine learning systems achieve superhuman performance in tasks such as language processing and image/video recognition, they do so using extremely large datasets and consume huge amounts of power. On the other hand, the brain remains superior in several cognitively challenging tasks while operating with the energy of a small lightbulb. To explore how biology achieves such high efficiency, we built a biologically constrained spiking neural network model and assessed its learning capacity on various discrimination tasks. We found that a form of structural plasticity, namely the ability of synapses to form and eliminate continuously, results in higher performance accuracy and faster learning across all scenarios tested. These improvements are most significant under more difficult learning conditions, such as when the number of trainable parameters is halved or when the task difficulty is increased. The study highlights the important role of structural plasticity in optimizing learning in biological circuits and opens new avenues for exploring the applicability of such biological plasticity rules in machine learning applications.

关键词： dendrites spiking neural network structural plasticity synaptic turnover

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IoT-Driven image Recognition for Microplastic Analysis in Water Systems using Convolutional neural networks 2

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

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2nd International conference on Computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional neural networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

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V-DETR: Pure Transformer for End-to-End Object Detection 16th

V-DETR: Pure Transformer for End-to-End Object Detection

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16th Asian conference on Intelligent Information and Database Systems (ACiiDS)

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Hue Univ Hue Univ Sci Hue City 530000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819749843;9789819749850

In the field of computer vision, the task of object detection is one of the important tasks, with many challenges and practical applications. Its task is to classify and determine the location of objects in images or videos. Many machine learning methods, especially deep learning, have been developed to perform this task. This article introduces a model combining DETR (DEtection TRansfomer) and ViT (Vision Transformer) as a method to recognize objects in images/videos that only use components of the Transformer model. The DETR model achieves good results in object detection using the Transformer architecture and without the need for complex intermediate steps. The ViT model, a Transformer-based architecture, has brought about a breakthrough in image classification. Combining both architectures opens exciting prospects in computer vision. The input image automatically extracted features using the ViT model previously trained on the imageNet21K dataset, then the features will be fed into the Transformer model to find the classification and bounding box of the objects. Experimental results on test data sets showthat this combined model has better ability in object recognition than DETR and ViT alone. This brings important prospects for the application of the Transformer model not only in the field of natural language processing but also in the field of image classification and object detection. The results of our proposed model have quite high mAP@0.5= 0.444 accuracy, slightly better than the original DETR model. The code is available at https://***/nguyendung622/vitdetr.

关键词： Computer Vision Object Detection Classification Convolutional neural networks Deep Learning DETR ViT

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