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

共 20880 条记录，以下是4851-4860 订阅

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Sports Video Classification Using Convolutional neural Network (CNN) with Normalization Flow

Sports Video Classification Using Convolutional Neural Netwo...

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International conference on artificial Intelligence and Data Sciences (AiDAS)

作者： Nurul. A. Emran Nurul Izrin Md Saleh Muhamad Zaidi Mohd Ali Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Melaka Malaysia Politeknik Seberang Perai Permatang Pauh Pulau Pinang Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Classifying sports videos is a crucial task in various applications, especially sports analytics, video retrieval, and content analysis. Unlike image classification, video classification is more challenging due to the dynamic nature of videos. Advancements in deep learning, particularly Convolutional neural Network (CNN), have shown promising results in sports video classification. In this paper, we present the results of implementing CNN with normalization flow to classify several sports classes. In particular, the effect of classification accuracy in training datasets by increasing the number of classes (with similar characteristics and some noise) is analyzed. The effect of frame averaging, and the number of epochs were also observed. The results show that for the training dataset, CNN performance is slightly affected by the additional classes, but by increasing the number of epochs, the accuracy of training and validation datasets has improved. CNN can still maintain highly accurate classifications in test datasets (more than 80 % ) in some observations. CNN with frame averaging shows lower errors than single-frame CNN.

关键词： Training Accuracy Sensitivity Noise neural networks Robustness Convolutional neural networks Sports Videos image classification

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6.9 A 0.35V 0.367TOPS/W image Sensor with 3-Layer Optical-Electronic Hybrid Convolutional neural Network

6.9 A 0.35V 0.367TOPS/W Image Sensor with 3-Layer Optical-El...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Xuecheng Wang Zheng Huang Tianyi Liu Wanxin Shi Hongwei Chen Milin Zhang Tsinghua University Beijing China

Traditional computer-vision technology that relies on image sensors coupled with cloud processing or on-chip artificial Intelligence (AI) processors have encountered significant challenges in terms of power consumption, delays arising from data transmission, and/or memory access. In-sensor and near-sensor computing have been reported to solve this issue by applying pixel or array level feature extraction [2–6]. In [2, 4], capacitors are utilized for analog-domain Haar filtering to reduce the processing power consumption, but sacrificing the Fill Factor (FF) due to the use of the capacitor array [2], or complicated pixel-level logic [4]. An image sensor in introduced in [3] with Hog feature-based object detection, achieving both low power and high accuracy for the detection of up to three object classes. However, it does not work on more complex tasks. Convolutional neural networks (CNNs) [5, 6] offer enhanced accuracy in practical applications, but increase the power consumption and circuit complexity. Optical-domain processing [7] is capable of parallel information processing through an optical architecture based on free space. However, in [7] this is based on diffraction and requires coherent light as input, which constrains the practicality for edge computing. This paper presents a Pulse Width Modulation (PWM) pixel-based image sensor array that integrates an optical-electronic hybrid 3-layer convolutional processing unit. The first layer of the convolution is performed in the optical domain without introducing extra power consumption. The integration of optical convolution with electrical convolution offers the potential to leverage the energy-efficient advantages of optical convolution, while simultaneously providing the essential programmability needed to address multiple use-cases through reprogramming within a single device. A dual-mode processing Element (PE) is proposed enables both acquisition mode and calculation mode. The integration of a sum/differenti

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A CNN Architecture for Detection and Segmentation of Colorectal Polyps from CCE images 5

A CNN Architecture for Detection and Segmentation of Colorec...

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5th IEEE International conference on image processing applications and Systems (IPAS)

作者： Tashk, Ashkan Sahin, Kasim E. Herp, Jurgen Nadimi, Esmaeil S. Univ Southern Denmark SDU Unit Appl AI & Data Sci AID Odense Denmark Univ Suthern Denmark SDU Dept Software Engn Odense Denmark

ISBN: (纸本)9781665462198

Colon capsule endoscopy (CCE) as a novel 2D biomedical image modality based on visible light provides a higher perspective of the potential gastrointestinal lesions like polyps within the small and large intestines than the conventional colonoscopy. As the quality of images acquired via CCE imagery is low, so the artificial intelligence methods are proposed to help detect and localize polyps within an acceptable level of efficiency and performance. In this paper, a new deep neural network architecture known as AID-U-Net is proposed. AID-U-Net consists of two distinct types of paths: a) Two main contracting/expansive paths, and b) Two subcontracting/expansive paths. The playing role of the main paths is to localize polyps as the target objectives in high resolution and multi-scale manner, while the two sub paths are responsible for preserving and conveying the information of low resolution and low-scale target objects. Furthermore, the proposed network architecture provides simplicity so that the model can be deployed for real time processing. AID-U-Net with an implementation of a VGG19 backbone shows better performance to detect polyps in CCE images in comparison with the other state-of-the-art U-Net models like conventional U-Net, U-Net++, and U-Net3+ with different pre-trained backbones like imageNet, VGG19, ResNeXt50, Resnet50, InceptionV3 and InceptionResNetV2.

关键词： AID-U-Net colon capsule endoscopy (CCE) Convolutional neural Network (CNN) Polyps U-Net

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Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bidirectional GRU-LSTM

Hybrid Deep Learning for Automatic Gauge Reading with CNN-Bi...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Hitesh Ninama Jagdish Raikwal Institute of Engineering and Technology Devi Ahilya University Indore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Automatic gauge reading plays a vital role in industrial automation, enabling efficient and accurate monitoring of parameters such as pressure, temperature, and flow. Traditional image processing struggles with diverse gauge designs, inconsistent lighting, and environmental factors. This study introduces a hybrid deep learning approach, combining Convolutional neural networks (CNNs) with Recurrent neural networks (RNNs) like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, to enhance accuracy and robustness. Five hybrid models, including CNN-LSTM, CNN-GRU, and their bidirectional versions, were evaluated using synthetic and real-world *** best performing model, CNN with SpatialDropout2D and Bidirectional GRU-LSTM (Model E), achieved a Mean Absolute Error (MAE) of 0.030, Root Mean Squared Error (RMSE) of 0.041, and an R 2 score of 0.991. It maintained high accuracy in various conditions, with an average inference time of 25 milliseconds per image, demonstrating suitability for real-time applications.

关键词： Deep learning Training Technological innovation Accuracy Temperature Robustness Real-time systems Convolutional neural networks Monitoring Long short term memory

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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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Partition-based neural Network Weight Parameter Mapping Method 4

Partition-based Neural Network Weight Parameter Mapping Meth...

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4th International conference on Natural Language processing, ICNLP 2022

作者： Wu, Jin Pang, Wenting Yang, Ling Gao, Yaqiong Xi'an University of Posts and Telecommunication Dept. School of Electronic Engineering Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

At present, with the continuous development of the application of artificial intelligence, it is no longer the computing speed of the processor that affects the performance of the neural network accelerator system. With the increasing complexity of data types, the energy consumed in executing the data computing process is far less than that consumed by data moving between the storage unit and the computing unit. Therefore, in order to minimize unnecessary data movement and reduce the energy loss caused by data movement, Memory-computing integration technology came into being at the historic moment. Its ultimate goal is to break through the traditional Von Neumann computer architecture and integrate the functions of memory and logic computing. The main computing process of neural network includes convolution layer and pooling layer operation. However, the frequent movement of weight data causes great unnecessary power consumption. Therefore, in-memory computing technology is very suitable for neural network applications, and for neural networks with larger weights, in-memory computing technology can show more advantages. This paper mainly introduces the architecture of neural network accelerator based on in-memory computing and two mapping methods of neural network weight parameters on this architecture. © 2022 IEEE.

关键词： Mapping

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Vulnerability Analysis of Vision Backbone networks: A Retrospect on Transfer Learning

Vulnerability Analysis of Vision Backbone Networks: A Retros...

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Future of Information and Communication conference, FICC 2025

作者： Duru, Chukwuemeka Masa-Ibi, Emmanuel Regmi, Bikesh Nyarko, Kofi Morgan State University Baltimore United States

ISBN: (纸本)9783031853623

Numerous organizations have adopted artificial Intelligence (AI) in one way or another, reflecting its growing popularity in both the academic and industrial sectors. At the core of these systems lies Deep neural networks, a machine learning technique influenced by human neurons. Due to the increasing dependence on these systems, their autonomous nature, and the fact that in some instances they control critical infrastructures and make decisions affecting people’s health, safety, and economic disposition, a thorough investigation into their vulnerability is paramount. This study examines the vulnerability of vision backbone networks to modern adversarial attacks given numerous deep learning applications that inherit learned features of these backbone networks through finetuning and transfer learning. The vulnerability of vision backbone networks like VGG16, Resnet50, and InceptionV3 against FGSM (Fast Gradient Sign Method), PGD (Projected Gradient Descent), CW (Carlini-Wagner), DeepFool, and One-pixel attack respectively are investigated. A new measure of imperceptibility called perceptual noise is established that measures the strength of the attack method and the robustness of these backbone networks. A custom image classifier network was trained as a baseline to evaluate the robustness of the backbone networks and all models under investigation achieved average accuracies above 90%, the custom model exhibited relatively lower vulnerability to adversarial attacks. In contrast, the pre-trained models showed higher vulnerability, likely due to transfer learning. This increased vulnerability is attributed to the transferred weights from the backbone networks having distributions reasonably distant from the training dataset distribution. As a result, the pre-trained models exhibited lower perceptual noise highlighting the impacts of learned weights on network robustness during transfer learning. © The Author(s), under exclusive license to Springer Nature Switzerland A

关键词： Adversarial machine learning

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Monocular 3D Human Pose Estimation Based on Global Temporal-Attentive and Joints-Attention In Video 48

Monocular 3D Human Pose Estimation Based on Global Temporal-...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： He, Ruhan Xiang, Shanshan Tao, Peng Yu, Yongsheng Wuhan Textile University School of Computer and Artificial Intelligence Wuhan China Wuhan University of Technology State Key Laboratory of Silicate Materials for Architectures Wuhan China

ISBN: (纸本)9781728163277

Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video, which is widely used in many 3D applications. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local contextual relations of human motion and ignores human joint hierarchies. To address this problem, we propose a Global Temporal-Attentive and Joints-Attention network (GTAJA-Net). This method introduces a Global Attention Feature Integration (GAFI) module and a Motion Tree Fusion Decoder (MTFD) module on the basis of a temporally consistent mesh recovery system (TCMR). A GAFI consisting of a collection of temporal features obtains final temporal features carrying spatial information that enhances temporal correlation and refine the features of the current frame. Meanwhile, MTFD aims at modeling the joint level attention. MTFD considers pose estimation as a top-down hierarchical process similar to SMPL kinematic tree. Though conceptually simple, our GTAJA-Net outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets. Our code is available at https://***/xiangcece/GTAJA-Net. © 2023 IEEE.

关键词： 3D human pose estimation monocular video recurrent neural networks

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Skin Cancer Classification Using Different Backbones of Convolutional neural networks 35th

Skin Cancer Classification Using Different Backbones of Conv...

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35th International conference on Industrial, Engineering and Other applications of Applied Intelligent Systems (IEA/AIE)

作者： Huynh, Anh T. Van-Dung Hoang Vu, Sang Le, Trong T. Nguyen, Hien D. Univ Informat Technol Fac Software Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City Vietnam Univ Informat Technol Fac Informat Syst Ho Chi Minh City Vietnam Univ Informat Technol Fac Comp Sci Ho Chi Minh City Vietnam

ISBN: (纸本)9783031085307;9783031085291

Melanoma is the deadliest of many different types of skin cancer. Clinical screening is followed by dermoscopic analysis and histopathological examination in the diagnosis of melanoma. Melanoma is a type of skin cancer that is highly curable if caught early. A visual examination of the affected area of the skin is the first step in melanoma skin cancer diagnosis. Dermatologists use a high-speed camera to take dermatoscopic images of skin lesions, which have an accuracy of 65-80% in melanoma diagnosis without any additional technical support. This research shows how to classify skin cancer using skin lesion photos using an automated classification approach based on image processing techniques. By studying images of skin lesions, the classification system will be able to determine whether or not a patient has melanoma. The contribution of this paper includes testing many different backbones and input sizes on the CNN models to evaluate the accuracy of the model on the siim-isic dataset. The overall prediction rate of melanoma diagnosis was raised to 82-86% on Sensitivity.

关键词： Skin cancer Benign Malignant Melanoma CNN Deep learning

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Research on Fault Diagnosis and Parameter Identification of Bidirectional LLC Resonant Converter Based on Data Driven

Research on Fault Diagnosis and Parameter Identification of ...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Xiaorui Zhang Shuo Jiang Pingxin Ma Dongyang Luo Tian Zhao Fengping He China Academy of Space Technology(CAST) Institute of Telecommunication and Navigation Satellites Beijing China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

At present, few people have studied the fault diagnosis of bidirectional LLC resonant converters. In order to promote the development of digital and intelligent fault diagnosis of power converters, this article analyzes the structural and parametric fault characteristics of bidirectional LLC resonant converters, designs structural fault diagnosis algorithms based on Support Vector Machine (SVM) and Random Forest (RF), and compares the diagnostic results of the two algorithms. Among them, the accuracy of random forest fault diagnosis is $100 \%$, the accuracy of SVM fault diagnosis is $98.58 \%$, the accuracy of RF fault diagnosis is higher, and the fault diagnosis speed is faster; At the same time, a bidirectional LLC resonant converter resonant capacitor parameter identification algorithm based on Extreme Learning Machines (ELM) and Deep neural Network (DNN) was studied. As a deep learning algorithm, DNN has an identification error of less than $5 \%$ and significantly better performance than ELM. The data-driven bidirectional LLC resonant converter fault diagnosis and parameter identification carried out in this article have achieved good results, laying a foundation for subsequent engineering applications.

关键词： Fault diagnosis Support vector machines Radio frequency Parameter estimation Accuracy Resonant converters artificial neural networks Prediction algorithms Circuit faults Random forests

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