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

共 2642 条记录，以下是31-40 订阅

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

The proceedings contain 102 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct artificial Feed Forward neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional neural Network Frameworks to Identify COVID-19 Deceases by Using Medical images;non-invasive Technique for Detecting Glycosuria Through image processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different applications.

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

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Signup page using Face recognition authentication

Signup page using Face recognition authentication

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Modi, Akash Rayar, Prajwal V Kavya, A.G. Vaishnavi, R. Dravya, B.S. Tamilarisi, R. Alliance School of Advanced Computing Department of Computer Science Bengaluru India

ISBN: (纸本)9798331529833

Improvements in artificial intelligence and machine learning have led to the growing integration of face recognition technology for a wide range of applications, from security systems to social media. Three related uses of facial processing technology are examined in this abstract: face detection, face recognition, and authentication through a face-based signup page. Finding and differentiating human faces in an image frame requires the first step, face detection. It also recognizes the facial features and separates them from other items in the frame using computer vision algorithms. Histogram of Oriented Gradients (HOG), and the Haar Cascade, are the methods used for face recognition. Still, due to failure in extracting facial features in harsh conditions, we use the more recent deep learning models like Convolutional neural networks (CNNs) to do this project. Building on detection and face recognition compares the observed faces to known faces in a database to identify or to confirm a person's identity. To assess distinctive facial traits (embeddings) or features and increase recognition accuracy across a variety of circumstances, like varying lighting and angles, modern face recognition systems employ deep learning models, such as Face Net and Deep Face. The Facial Recognition The signup page serves as an example of how face recognition can be used in authentication systems for secure apps. A user registers for this program by having their face captured, and this information is safely kept and acts as a unique identification. The solution gives a hands-free and password-less authentication mechanism during login by comparing the captured face with the stored data in the database. © 2025 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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Web System for Recognizing Actions of Physical Violence in Urban Spaces Using CNN with Transfer Learning 11th

Web System for Recognizing Actions of Physical Violence in U...

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11th Latin American conference on High Performance Computing

作者： Diaz, Jose Edgar Garcia Romero, Wilder Suarez Rodriguez, Ciro de la Cruz, Jorge Puga Yalan, Victor Manuel Cabrejos Temoche, Isis Ayme Moran Carranza, Jhon Charlie Martinez Univ Nacl Federico Villarreal UNFV Escuela Univ Posgrado Lima 15001 Peru Univ Nacl Amazonia Peruana UNAP Facultad Ingn Sistemas & Informat Iquitos 16001 Peru Univ Nacl Mayor San Marcos UNMSM Dept Software Engn Lima 15081 Peru Inst Invest Amazonia Peruana IIAP Iquitos 16001 Peru

ISBN: (纸本)9783031800832;9783031800849

Violence is a problem of great impact that affects the countries of the world, violence is a soft system that to date cannot be controlled, because it can manifest itself in different ways, represented by high levels of crime and delinquency. But now, there is artificial Intelligence (AI), which uses innovative resources and will help close gaps in the Sustainable Development Goals (SDG), such as in health, traffic management, climate change and also violence. One way to apply AI is through image processing with Convolutional neural networks (CNN). This research evaluates the effectiveness of classifying actions of interpersonal violence such as: strangulation, grappling, kicking or punching, using CNN with Transfer Learning. First, a personalized dataset was developed using images with simulated violent actions, obtaining a total of 2000 images, which were distributed into 60% for training, 30% for validation and 10% for testing. Second, the pre-trained CNN models were trained: VGG16, MobileNetV2 and YOLOv8, subjected to 150 epochs, where the performance results were compared between them and it was determined that the best performance was for YOLOv8, obtaining an accuracy rate of 94.25% and an accuracy of 89%. In the end, a prototype web system was developed using Python with OpenCV and YOLOv8 to detect actions of interpersonal violence in real time and alert the authorities involved in a timely manner. This research will serve as a reference for future real-time video surveillance applications.

关键词： Physical violence Convolutional neural networks Web System Deep Learning

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Unveiling Cetacean Voices: Entropy-Powered Spectrogram Denoising for Deep Learning applications 23rd

Unveiling Cetacean Voices: Entropy-Powered Spectrogram Denoi...

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23rd EPIA conference on artificial Intelligence, EPIA 2024

作者： Bicudo, Francisco Cavaco, Sofia Freitas, Luís Verborgh, Philippe Esteban, Ruth Gauffier, Pauline Silva, Joaquim Nova School of Science and Technology Costa da Caparica Portugal Museu da Baleia da Madeira Caniçal Portugal

ISBN: (纸本)9783031734991

Acoustic monitoring of cetaceans is crucial for studying and conserving these animals and their environment. With the rising interest in deciphering dolphin and whale communication, and the promise shown by machine learning solutions in this field, the demand for gathering and processing large vocalization datasets is only increasing. In this paper, we propose an entropy-based spectrogram filtering method that removes noise naturally present in recordings of cetacean vocalizations. This method enhances the visual clarity of cetacean spectrograms, aiding biologists, and improves vocalization detection when using image-based convolutional neural networks. Its implementation focuses on efficiency, processing 256 times faster than similar filters. The resulting spectrograms achieve on average a 98.73% reduction in required storage space and allow for a segmentation technique that reduces labelling and classification times in machine learning solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spectrographs

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Generating Editable Head Avatars with 3D Gaussian GANs

Generating Editable Head Avatars with 3D Gaussian GANs

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Guohao Yang, Hongyu Men, Yifang Huang, Di Li, Weixin Yang, Ruijie Wang, Yunhong School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350368741

Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like neural Radiance Fields (NeRF), achieve photorealistic and view-consistent 3D head synthesis. However, these methods face limitations in deformation flexibility and editability, hindering the creation of lifelike and easily modifiable 3D heads. We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation. This method enables easier illumination control and improved editability. Central to our approach is the Editable Gaussian Head (EG-Head) model, which combines a 3D Morphable Model (3DMM) with texture maps, allowing precise expression control and flexible texture editing for accurate animation while preserving identity. To capture complex non-facial geometries like hair, we use an auxiliary set of 3DGS and tri-plane features. Extensive experiments demonstrate that our approach delivers high-quality 3D-aware synthesis with state-of-the-art controllability. Our code and models are available at https://***/liguohao96/EGG3D. © 2025 IEEE.

关键词： 3D-aware GAN image video synthesis

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InceptionNeXt Network with Relative Position Information for Microexpression Recognition 18th

InceptionNeXt Network with Relative Position Information fo...

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18th International conference on Wireless artificial Intelligent Computing Systems and applications, WASA 2024

作者： Cao, Zhilong Dong, Anming Yu, Jiguo Li, Sufang Tian, Xiang Zhang, Li Jinan250014 China Jinan250353 China Jinan250014 China

ISBN: (纸本)9783031714696

Micro-expression recognition is one of the most challenging tasks in affective computing, aiming to identify subtle facial movements that are difficult for humans to perceive within a short period. In recent years, convolutional neural networks (CNNs) have been widely employed for micro-expression recognition. Despite achieving high recognition accuracy, these approaches still exhibit some limitations. Traditional CNNs learn solely from complete images, failing to accurately capture the fine details of facial micro-expressions. Traditional convolution uses small convolution kernels, which ignore global image features during feature extraction, affecting the accuracy of micro expression recognition. In response to the shortcomings of convolutional kernels, this paper proposes a large kernel convolutional neural network for micro expression recognition. Specifically: 1) Introducing a large-kernel convolutional neural network (LKCNN) that extends the model’s receptive field, enabling it to capture data relationships over a larger range. This enhances the model’s performance. To balance computational costs, the network utilizes Inception depthwise convolution, which decomposes expensive depthwise convolutions into three convolutional branches with small kernel sizes, along with an identity mapping branch. 2) The input is transformed from entire images to image blocks, followed by the incorporation of relative position attention (RPI). This not only magnifies detailed features but also filters important feature information, ensuring the full utilization of subtle information. Experiments were conducted on three publicly available databases: CASME ii, SMIC, and SAMM. The results demonstrate superior performance of the proposed method in three-class and five-class recognition compared to state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Efficient Sparse Tensor Core networks for Real-Time Insect Classification in Agriculture 4th

Efficient Sparse Tensor Core Networks for Real-Time Insect C...

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4th International conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Rao, P. Kiran Prakash, P. Suman Kumar, N. Jaswanth Reddy, V. Kartheek Kumar, Arigela Satheesh Rajeev Gandhi Mem Coll Engn & Technol Dept CSE Data Sci Nandyal Andra Pradesh India G Pullaiah Coll Engn & Technol Autonomus Dept CSE Data Sci Kurnool Andra Pradesh India

ISBN: (纸本)9783031837890;9783031837906

Deep learning has become a powerful tool for various image-based applications, including the detection of agricultural pests. However, the deployment of deep neural networks in resource-constrained agricultural settings poses significant challenges due to their high computational demands. Traditional pruning methods designed to mitigate this challenge often result in sparse networks that may not perform efficiently on hardware optimized for dense operations. In this study, we present a novel approach called Efficient Sparse Tensor Core networks (STCN), which leverages the inherent sparsity of neural networks to enhance computational efficiency significantly. Our method integrates a specialized sparse network architecture with hardware-accelerated tensor operations, optimized for real-time analysis on edge devices. We have tailored our model to exploit sparse tensor operations, facilitating faster computations and reduced energy consumption without compromising accuracy. When applied to the IP102 dataset, a comprehensive collection of insect pest images, the STCN method proves effective in classifying various pest species under different environmental conditions. Initial experiments show that our approach not only maintains a high classification accuracy of 92% but also achieves a substantial reduction in computational workload, reducing energy consumption by 30% and improving processing speed by 40% during inference. These findings highlight the potential of STCN for real-time pest detection, paving the way for the practical application of advanced AI in precision agriculture, optimizing resource utilization, and promoting sustainable farming practices.

关键词： Sparse Tensor Core networks (STCN) Real-time Insect Pest Classification Agricultural AI Computational Efficiency Edge Computing Deep Learning Optimization

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Investigation of AI with OpenCV-Python for Detecting Diabetes

Investigation of AI with OpenCV-Python for Detecting Diabete...

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conference of Recent Trends and applications of Soft Computing in Engineering, RTASCE 2024

作者： Mutar, Hussein A. Yalwi, Ilyas Khudhair Mezaal, Yaqeen S. Th. Salim ALRikabi, Haider Alaidi, Abdul Hadi M. Niama ALRubeei, Ibtihal R. Magdy, Ahmed Electrical Engineering Department College of Engineering Wasit University Wasit Iraq College of Computer Science and Information Technology Wasit University Wasit Iraq College of Engineering University of Information Technology and Communications Baghdad Iraq Electrical Engineering Department Faculty of Engineering Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031828805

Plenty of different diagnosing methods have been extensively utilized to identify diabetes accurately;however, an absolutely precise and definitive diagnosis has not yet been attained. Within the context of this research, our primary objective is to leverage the cutting-edge capabilities of artificial Intelligence (AI) coupled with OpenCV to assist medical professionals, thereby minimizing the rate of misdiagnosis. Specifically, we harness the power of AI to effectively classify diverse images portraying patients afflicted with Non-Proliferative Diabetic Retinopathy (NPDR), with the ultimate goal of determining the severity level at which they are situated. In conjunction with this, Python, with OpenCV, has a crucial role in extracting pertinent features that may be discernible within the given images. Our methodology involves the collection and preprocessing of the Eye PACS Dataset on Kaggle, followed by feature extraction and model training using some machine learning algorithms, including convolutional neural Network CNN, decision trees, support vector machines SVM, and neural networks. OpenCV is utilized for image processing tasks, enhancing the feature extraction process, certain individual features present within the images are precluded from being considered as contributing factors in the classification process. Some of these features include but not limited to, the measurement of the luminous blobs which are present in the image, the specific area of existence of red lesion. The evaluation of the models includes the analysis of their performance based on the goal of the prediction task, specifically decimal-based accuracy, precision, recall, and F1-score. This research employs a wide-ranging dataset embracing low, medium and high level of image severity. At last, after lots of simulation, it came to a conclusion that the CNN increases its level of classification accuracy up to 98%. These findings show that the proposed application of AI improves the accuracy

关键词： Convolutional neural networks

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