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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"

共 6125 条记录，以下是321-330 订阅

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Residual learning and Deep learning Models for image Denoising in Medical applications

Residual Learning and Deep Learning Models for Image Denoisi...

引用

International conference on. Cryptology and Network Security with machine learning, ICCNSML 2023

作者： Srivastava, Atul Rana, Harshita Misra, Manoj Kumar Singh, Youddha Beer Amity School of Engineering and Technology AUUP Lucknow India Pranveer Singh Institute of Technology Kanpur India Galgotia College of Engineering and Technology UP Greater Noida India

ISBN: (纸本)9789819706402

The utilization of CT scans in medical diagnostics has seen a consistent and substantial rise. However, this increased usage has raised concerns regarding the potential harmful effects of radiation exposure on patients. Reducing the radiation dose can result in more noise in the captured images, which can negatively impact the radiologist's ability to make accurate judgments with confidence. The most commonly encountered types of noise in medical images include Gaussian noise, speckle noise, and salt and pepper noise. Numerous significant efforts have been made to enhance image quality by eliminating this noise, and deep learning-based methods have gained popularity due to their effectiveness in handling various types of noise and image datasets. Within the research community, various neural network variations, such as autoencoders, generative adversarial networks (GANs), residual networks, convolutional neural networks (CNNs), and regularized neural networks, have gained immense popularity. In this paper, we comprehensively discuss eleven highly impactful approaches for image denoising based on deep learning techniques. We assess the performance of these methods using two quantitative and effective metrics: structural SIMilarity (SSIM) and peak signal-to-noise ratio (PSNR). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Classification-Based and Lightweight networks for Fast image Super Resolution 1

引用

32nd International conference on Artificial neural networks (ICANN)

作者： Zhong, Xueliang Luo, Jianping Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Coll Elect & Informat Engn Shenzhen Peoples R China

ISBN: (数字)9783031442100

ISBN: (纸本)9783031442094;9783031442100

Lightweight image super-resolution (SR) networks are of great significance for practical applications. Presently, there are several SR methods based on deep learning with excellent performance, but their memory and computation costs hinder practical applications. In this paper, we propose a down-up sampling continuous mutual affine super-resolution network (DUSCMAnet) to solve above problems. Moreover, we propose a classification-based SR algorithm based on image statistical features (TSClassSR-DUSCMAnet) for accelerating SR networks on large images (2K-8K). The proposed algorithm first decomposes the large images into small sub-images, then uses a Class-Module to classify sub-images into different classes according to the difficulty of reconstruction, then use a SR-Module to perform SR for different classes. The Class-Module is composed of a support vectormachine (SVM) based on image statistical features, and the SR-Module is composed of our proposed DUSCMAnet, a lightweight SR network. After classifying, a majority of sub-images will pass through lighter networks, thus the computational cost can be significantly reduced. Experiments show that our DUSCMAnet is superior to the existing lightweight SR models in terms of time performance and also has competitive SR performance. Our TSClassSR-DUSCMAnet can help DUSCMAnet save up to 63% FLOPs on DIV8K datasets.

关键词： image Super-Resolution Deep learning machine learning

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Target-Aware Siamese networks Based on Masked Attention Mechanism for Visual Object Tracking 7

Target-Aware Siamese Networks Based on Masked Attention Mech...

引用

7th IEEE International conference on Multimedia Information processing and Retrieval (MIPR)

作者： Su, Yao-Hui Shieh, Ming-Der Tsai, Chia-Chi Natl Cheng Kung Univ Elect Engn Tainan Taiwan

ISBN: (纸本)9798350351439;9798350351422

Visual object tracking(VOT) is a popular and fundamental task in the field of computer vision, which aim to locate the target object in a video sequence. VOT plays a crucial part in various applications, such as unmanned aerial vehicle (UAV), autonomous driving, surveillance, and human-computer interaction, etc. However, when applying visual object tracking in real-world scenarios, numerous challenges arise due to environmental factors. These challenges include cluttered backgrounds, object deformations, illumination variation, and more. Therefore, addressing these challenges is crucial for modern visual object trackers. In recent years, VOT algorithms based on Siamese neural networks have achieve a great tradeoff between tracking performance and computational complexity, making it popular frameworks for visual object tracking. Siamese-based tracks rely on comparing similarity between a template image of target and the of the search region image to achieve object tracking. However, these Siamese-based trackers relying on similarity matching may lead to false tracking duo to its insufficient discriminative ability in challenging scenarios such as cluttered backgrounds. This work proposes a Siamese-based tracker with masked attention mechanism, which aims to enhance the target features representation and improve the discriminative ability of Siamese-based tracker when dealing with cluttered backgrounds. Finally, we evaluated the performance of the proposed Siamese-based visual object tracker using the OTB100 testing dataset. The experimental results demonstrated improved performance in challenging scenes such cluttered backgrounds scenario.

关键词： visual object tracking siamese neural networks attention mechanism deep learning similarity learning

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Research on Automobile Fault Diagnosis Model Based on neural Network 4

Research on Automobile Fault Diagnosis Model Based on Neural...

引用

4th IEEE International conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Zhai, Shaoqing Wang, Zihan School of Automation Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359589

With the continuous development and popularization of automotive technology, modern automobiles are equipped with a large number of electronic and mechatronic systems, which makes the diagnosis of automotive faults more complex. The traditional manual detection methods can no longer meet the increasing demand for automobile maintenance. In this study, a neural network-based fault diagnosis algorithm model for automobile external deformation is proposed. In this paper, a dataset containing 8000 images of faulty family minivans and 4000 images of normal cars is constructed. In order to improve the image processing effect, this paper adopts bilateral filtering technique for image denoising to improve the image quality and make it more suitable for subsequent processing. In this paper, ordinary convolutional neural network and DenseNet network models are established respectively. And through model validation, it is found that when DenseNet-264 network is chosen, the model has the lowest error rate and higher accuracy, and the prediction error rate for faulty cars is only 20.6%. © 2024 IEEE.

关键词： Automotive fault diagnosis Computer vision Deep learning neural networks

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Palm Tree Diseases Detection Using Deep learning: A Short Review 8

Palm Tree Diseases Detection Using Deep Learning: A Short Re...

引用

8th IEEE International conference on image and Signal processing and their applications, ISPA 2024

作者： Bouthaina, Rechachi Rezeg, Khaled Okba, Kazar Merizig, Abdelhak Mohamed Khider University LINFI Laboratory Biskra Algeria Sharjah University Computing and Informatics College Arts Sciences & Information Technology College Kalba United Arab Emirates

ISBN: (纸本)9798350309249

Agriculture is a sector that represents a real indicator of the country's development, and it is a sector that affects national security. The palm tree is a significant, affordable, and nutrient-rich food source that gives millions of people access to wholesome food globally. It can get a lot of illnesses, much like other plants. DL has been effectively implemented in agriculture and has been employed in many other industries. This paper examines the latest advancements in the use of deep learning (DL) for the diagnosis of palm tree diseases. Particularly Convolutional neural networks (CNN) are often utilised in agricultural research due to its strong image processing ability. © 2024 IEEE.

关键词： Precision agriculture

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Deep learning Based Motion Target Detection Algorithm 4

Deep Learning Based Motion Target Detection Algorithm

引用

4th IEEE International conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Wang, Xizhou School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359589

With the dramatic growth of video data, the storage and computational resources required to process this huge amount of data have increased significantly. In order to cope with this challenge, it is necessary to extract the key information in the video in a more intelligent and efficient way, while filtering out a large amount of redundant content. In this paper, the traditional CNN model and Transformer model are constructed respectively using video frames of car motion process from video viewpoint as a dataset. The model performance is improved by advanced data preprocessing operations. The bilateral filtering technique is introduced in this study, aiming to improve the image quality and enhance the image processing effect through denoising operations, making it more applicable to the subsequent processing steps. Finally, the Transformer model is verified by the model and the recognition accuracy of the Transformer model is up to about 90%. © 2024 IEEE.

关键词： Convolutional neural networks Deep learning image processing Target detection

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Finding Identity through Gait 6

Finding Identity through Gait

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Srinivasan, Lavanya University of West London School of Computing and Engineering London United Kingdom

ISBN: (纸本)9798331506520

Human gait identification is the recognition of a person from a series of walking images. In contrast to fingerprint or iris-based identification methods, gait identification offers the significant benefit of remote execution. Gait detection is emerging as one of the most promising biometric identification techniques. Traditional methods to identify the identity of gait are Background subtraction, Gait Energy image, Gait Entropy, etc., human gait identification is a potential new tool for identifying individuals beyond traditional methods. The objectives of this study were to develop an automated gait detection system and examine the unique aspects of gait. This study encompassed the following: gait predictions and estimation based on HRNet 17 joint positions (locating body joints of humans in an image/video), better predictions with higher confidence scores by eliminating poor predictions, and the 14 joint coordinates and 17 joint points are fed into Graph Convolutional Network by eliminating low confidence pose to classify labels. Semi-supervised learning on graph-structured data can be accomplished via Graph Convolutional networks. Graph Convolutional networks is a very effective version of convolutional neural networks, which function directly on graphs. The experimental results show that the Graphical Convolutional Network identifies gait subjects with higher accuracy of normal walking and carrying a backpack with 98% and 96% respectively. This work will be extended in the future by fusing face and gait to identify various gait subjects and the gait in forensic identifications. © 2025 IEEE.

关键词： Convolutional neural networks

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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...

引用

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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Causal Analysis for Robust Interpretability of neural networks

Causal Analysis for Robust Interpretability of Neural Networ...

引用

IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Ahmad, Ola Bereux, Nicolas Baret, Loic Hashemi, Vahid Lecue, Freddy CortAIx Thales Digital Solut Montreal PQ Canada Paris Saclay Univ Paris France AUDI AG Ingolstadt Germany INRIA Sophia Antipolis France

ISBN: (纸本)9798350318920;9798350318937

Interpreting the inner function of neural networks is crucial for the trustworthy development and deployment of these black-box models. Prior interpretability methods focus on correlation-based measures to attribute model decisions to individual examples. However, these measures are susceptible to noise and spurious correlations encoded in the model during the training phase (e.g., biased inputs, model overfitting, or misspecification). Moreover, this process has proven to result in noisy and unstable attributions that prevent any transparent understanding of the model's behavior. In this paper, we develop a robust interventional-based method grounded by causal analysis to capture cause-effect mechanisms in pre-trained neural networks and their relation to the prediction. Our novel approach relies on path interventions to infer the causal mechanisms within hidden layers and isolate relevant and necessary information (to model prediction), avoiding noisy ones. The result is task-specific causal explanatory graphs that can audit model behavior and express the actual causes underlying its performance. We apply our method to vision models trained on classification tasks. On image classification tasks, we provide extensive quantitative experiments to show that our approach can capture more stable and faithful explanations than standard attribution-based methods. Furthermore, the underlying causal graphs express the neural interactions in the model, making it a valuable tool in other applications (e.g., model repair).

关键词： accountable Algorithms Algorithms and algorithms ethical computer vision Explainable fair formulations machine learning architectures privacy-preserving

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Quantizing neural networks with Knowledge Distillation for Efficient Video Quality Assessment

Quantizing Neural Networks with Knowledge Distillation for E...

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2024 conference on Visual Communications and image processing

作者： Yu, Jiayuan Li, Yingming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798331529543;9798331529550

No-Reference Video Quality Assessment (NR-VQA) refers to evaluating the perceptual quality of videos without access to the original reference videos. However, existing deep NR-VQA methods often entail significant computational overhead, posing challenges in deploying these models in practical applications. In this paper, we focus on developing lightweight no-reference video quality assessment models and propose an Enhanced Quantization with Knowledge Distillation (EQKD) strategy to perform model compression while maintaining competitive performance. Specifically, we first introduce quantizationaware training in the VQA model parameter learning, which significantly reduces the memory footprint of the VQA models. Additionally, we incorporate knowledge distillation into the training process to further enhance the performance. The experimental results on three public datasets demonstrate the superiority of the proposed models over the state-of-the-art.

关键词： video quality assessment quantization knowledge distillation

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