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2022 IEEE International conference on artificial Intelligence and Computer applications, ICAICA 2022

作者： Ma, Boyang Datang Northeast Electric Power Test & Research Institute Co.Ltd. Changchun China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Considering the learning advantage of reservoir computing, the echo state network is introduced to replace the full connection layer of the traditional convolutional neural network, and thus, an improved convolutional neural network is proposed for failure detection classification of boiler four tube in this paper. For the failure detection of boiler four tube, due to the difficulty of manual classification, it is necessary to classify the collected pictures by using an improved intelligent classification method. According to the image information processing ability of the convolutional neural network, the image feature information of boiler four tube can be extracted. Because the extracted image feature information is introduced into the reservoir, the failure detection type of boiler four tube can be classified more accurately by using the learning ability of echo state network. In order to reduce the influence of the reservoir parameters on the classification accuracy of the proposed method, a parameter optimization method is given for optimizing the image classification model. Through using the collected images, the simulation results show that the classification accuracy of the proposed classification method reaches 90.1%. © 2022 IEEE.

关键词： Convolutional neural networks

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An Effective Unsupervised Method for Change Detection in SAR images 14

An Effective Unsupervised Method for Change Detection in SAR...

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14th International conference on Graphics and image processing (ICGIP)

作者： Liu, Qianqian Xue, Xiaorong Liu, Kejun Liu, Haiyan Liaoning Univ Technol Sch Elect & Informat Engn Jinzhou Liaoning Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Synthetic aperture radar (SAR) image change detection has a very important and wide applications, and the complexity of SAR image data makes the accurate identification of change regions a challenging task. Many Convolutional neural networks (CNN) have been successfully applied to change detection tasks, but most of them have complex structures and heavy computation, so this paper proposes an unsupervised lightweight Convolutional neural Network for change detection. We introduce conditionalized convolution (CondConv) and ConvNeXt block into the change detection task to improve the performance of the network in recognizing complex objects, and we can refer to this method as DNNet for short. In this paper, the bi-temporal images are first subjected to differential analysis, and then the convolutional neural network is trained with pseudo-labels generated by clustering. The effectiveness of DNNet is verified on three real SAR datasets and compared with several state-of-the-art methods. Experiments results show that the proposed method has better effectiveness.

关键词： SAR unsupervised change detection convolutional neural networks conditionalized convolution

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A Deep Learning Model Based on CNN Using Keras and TensorFlow to Determine Real-Time Melting Point of Chemical Substances 4th

A Deep Learning Model Based on CNN Using Keras and TensorFlo...

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4th International conference on Machine Intelligence and Signal processing, MISP 2022

作者： Shrivastava, Anurag Sushil, Rama School of Computing DITU Dehradun India

ISBN: (纸本)9789819900848

Deep learning is a subset of machine learning that uses artificial neural networks inspired by human cognitive systems. In many applications, deep learning becomes most successful approach where machine learning has been successful at certain rates. In the succession of these, the proposed deep learning model is suitable for melting-point detection apparatus which determines melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classifies images of chemical’s state (solid or liquid) by deep neural network (DNN), and it consists of TensorFlow framework, libraries like Keras and activation function like ReLu, sigmoid, maxpool and flatten to determine melting point of chemical substances. The proposed model enables to TensorFlow architecture, which can determine the melting point of chemicals in real time on a single board computer. The input image data mainly focuses on chemical’s state, and there are two categories of chemical’s state either solid or liquid. The results discussed in terms of the image classification accuracy in percentage. The images from two class label get maximum accuracy is 99.72% and maximum validation accuracy is 99.37% same as liquid’s image and the average value of accuracy 84.17% or higher after certain epochs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Recognizing Indian Classical Dance Forms Using Transfer Learning 4th

Recognizing Indian Classical Dance Forms Using Transfer Lear...

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4th International conference on Machine Intelligence and Signal processing, MISP 2022

作者： Reshma, M.R. Kannan, B. Jagathyraj, V.P. Sabu, M.K. Department of Computer Applications CUSAT Kerala Kochi India School of Management Studies CUSAT Kerala Kochi India

ISBN: (纸本)9789819900466

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. Computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A deep learning framework for early diagnosis of Alzheimer's disease on MRI images

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MULTIMEDIA TOOLS AND applications 2023年第2期83卷 3767-3799页

作者： Arafa, Doaa Ahmed Moustafa, Hossam El-Din Ali, Hesham A. Ali-Eldin, Amr M. T. Saraya, Sabry F. Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura Egypt Mansoura Univ Fac Engn Elect & Commun Engn Dept Mansoura Egypt Delta Univ Sci & Technol Fac Artificial Intelligence Mansoura Egypt

Numerous medical studies have shown that Alzheimer's disease (AD) was present decades before the clinical diagnosis of dementia. As a result of the development of these studies with the discovery of many ideal biomarkers of symptoms of Alzheimer's disease, it became clear that early diagnosis requires a high-performance computational tool to handle such large amounts of data, as early diagnosis of Alzheimer's disease provides us with a healthy opportunity to benefit from treatment. The main objective of this paper is to establish a complete framework that is based on deep learning approaches and convolutional neural networks (CNN). Four stages of AD, such as (I) preprocessing and data preparation, (ii) data augmentation, (iiI) cross-validation, and (IV) classification and feature extraction based on deep learning for medical image classification, are implemented. In these stages, two methods are implemented. The first method uses a simple CNN architecture. In the second method, the VGG16 model is the pre-trained model that is trained on the imageNet dataset but applies the same model to the different datasets. We apply transfer learning, meaning, and fine-tuning to take advantage of the pre-trained models. Seven performance metrics are used to evaluate and compare the two methods. Compared to the most recent effort, the proposed method is proficient of analyzing AD, moreover, entails less labeled training samples and minimal domain prior knowledge. A significant performance gain on classification of all diagnosis groups was achieved in our experiments. The experimental findings demonstrate that the suggested designs are appropriate for basic structures with minimal computational complexity, overfitting, memory consumption, and temporal regulation. Besides, they achieve a promising accuracy, 99.95% and 99.99% for the proposed CNN model in the classification of the AD stage. The VGG16 pre-trained model is fine-tuned and achieved an accuracy of 97.44% for AD stage clas

关键词： Alzheimer's Disease (AD) Convolution neural Network (CNN) Deep Learning (DL) Transfer Learning (TL) Imaging Pre-processing

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Unsupervised Pose Estimation by Means of an Innovative Vision Transformer 21st

Unsupervised Pose Estimation by Means of an Innovative Visio...

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21st International conference on artificial Intelligence and Soft Computing (ICAISC)

作者： Brandizzi, Nicolo' Fanti, Andrea Gallotta, Roberto Russo, Samuele Iocchi, Luca Nardi, Daniele Napoli, Christian Sapienza Univ Rome Dept Comp Automat & Management Engn Via Ariosto 25 I-00185 Rome Italy Sapienza Univ Rome Dept Psychol Via Marsi 78 I-00185 Rome Italy

ISBN: (纸本)9783031234798;9783031234804

Attention-only Transformers [34] have been applied to solve Natural Language processing (NLP) tasks and Computer Vision (CV) tasks. One particular Transformer architecture developed for CV is the Vision Transformer (ViT) [15]. ViT models have been used to solve numerous tasks in the CV area. One interesting task is the pose estimation of a human subject. We present our modified ViT model, Un-TraPEs (UNsupervised TRAnsformer for Pose Estimation), that can reconstruct a subject's pose from its monocular image and estimated depth. We compare the results obtained with such a model against a ResNet [17] trained from scratch and a ViT finetuned to the task and show promising results.

关键词： Computer vision image understanding Pose estimation Visual transformers artificial intelligence and applications

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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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Energy-Guided Continuous Entropic Barycenter Estimation for General Costs 38

Energy-Guided Continuous Entropic Barycenter Estimation for ...

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38th conference on neural Information processing Systems, NeurIPS 2024

作者： Kolesov, Alexander Mokrov, Petr Udovichenko, Igor Gazdieva, Milena Pammer, Gudmund Kratsios, Anastasis Burnaev, Evgeny Korotin, Alexander Skolkovo Institute of Science and Technology Artificial Intelligence Research Institute Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Graz University of Technology Graz Austria Vector Institute McMaster University Ontario Canada

Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter task is to take the average of a collection of probability distributions w.r.t. given OT discrepancies. We propose a novel algorithm for approximating the continuous Entropic OT (EOT) barycenter for arbitrary OT cost functions. Our approach is built upon the dual reformulation of the EOT problem based on weak OT, which has recently gained the attention of the ML community. Beyond its novelty, our method enjoys several advantageous properties: (i) we establish quality bounds for the recovered solution;(ii) this approach seamlessly interconnects with the Energy-Based Models (EBMs) learning procedure enabling the use of well-tuned algorithms for the problem of interest;(iii) it provides an intuitive optimization scheme avoiding min-max, reinforce and other intricate technical tricks. For validation, we consider several low-dimensional scenarios and image-space setups, including non-Euclidean cost functions. Furthermore, we investigate the practical task of learning the barycenter on an image manifold generated by a pretrained generative model, opening up new directions for real-world applications. Our code is available at https://***/justkolesov/EnergyGuidedBarycenters. © 2024 neural information processing systems foundation. All rights reserved.

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CropViT: A light-weight Transformer Model for Crop Disease Detection 3

CropViT: A light-weight Transformer Model for Crop Disease D...

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3rd International conference on artificial Intelligence For Internet of Things, AiioT 2024

作者： Chemmalar Selvi, G. Charan, H.J. Kumar, Dinesh Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Agricultural industry has grown significantly bring sustainable farming practices in improving the food quality, enhancing agricultural productivity and global food security. However, the crop yield and its quality are impacted when the timely identification and management of the crop diseases are not promptly solved. Thus, the crop disease detection becomes the critical aspect in the smart agriculture. Several researchers addressed this crop disease detection by developing optimized solutions through machine learning, deep learning and image processing techniques. However, these techniques face serious challenges when deployed in real-time crop disease identification problems like annotated training images, capturing long-range dependencies, adaption to real-time changes evolving over time and so on. This leads to performance degradation seeking for more powerful applications like some advanced deep learning techniques, in particular, the transformer models. In this paper, a novel crop disease detection model known as CropViT was proposed based on Vision Transformer. This CropViT was developed by fine-tuning the architecture of existing Vision Transformer and PlantVillage dataset was used in the experimental study. Nine crop species were selected and trained using CropViT whose performance was then compared with traditional Convolutional neural Network model. The experimental study clearly showed the highest mean accuracy of 98.64% for CropViT when compared to 95.52% mean accuracy for traditional CNN. The experimental results also outperformed other existing state-of-the-art models. Finally, the work was concluded with the discussion of experimental results and its future scope of work. © 2024 IEEE.

关键词： Convolutional neural networks

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Continuous Differential image-based Fast Convolution for Convolutional neural networks 13

Continuous Differential Image-based Fast Convolution for Con...

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13th International conference on Information and Communication Technology Convergence, ICTC 2022

作者： Hong, Sunghoon Park, Daejin School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Convolutional neural networks with powerful visual image analysis of deep structures are gaining popularity in many research fields. The main difference in convolutional neural networks compared to other artificial neural networks is the addition of many convolutional layers. The convolutional layer improves the performance of artificial neural networks by extracting feature maps required for image classification. However, for applications that require very low-latency on limited processing resources, the success of a convolutional neural network depends on how fast we can compute. In this paper, we propose a novel convolution technique of fast algorithms for convolutional neural networks using continuous differential images. The proposed method improves the response speed of the algorithm by reducing the computational complexity of the convolutional layer. It is compatible with all types of convolutional neural networks, and the lower the difference in the continuous images, the better the performance. We use the darknet network to benchmark the CPU implementation of our algorithm and show state-of-the-art throughput at pixel difference thresholds from 0 to 25 pixels. © 2022 IEEE.

关键词： Convolutional neural networks

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