检索结果-内蒙古大学图书馆

Enhanced inter-camera person re-identification leveraging mixed-order relation-aware recurrent neural network

NEUROCOMPUTING 2025年 638卷

作者： Vidhyalakshmi, M. K. Unhelkar, Bhuvanesh Kshirsagar, Pravin R. Thiagarajan, R. SRM Inst Sci & Technol Fac Engn & Technol Dept Comp Technol Kattankulathur 603203 Tamil Nadu India Univ S Florida Muma Coll Business Sch Informat Syst & Management Dept Informat Technol Sarasota Manatee Campus Sarasota FL USA JD Coll Engn & Management Dept Elect & Telecommun Engn Nagpur India Prathyusha Engn Coll Dept Informat Technol Chennai India

The person Re-Identification (Re-ID) requires a significant quantity of the costly label information, whereas unsupervised ones are still unable to provide satisfactory identification performance. These results in the poor scalability due to the requirement of the laborious data collection and annotation process in real-world Re-id applications. Unsupervised Re-ID techniques not require identity label data, but have significantly worse and inadequate model performance. In this paper, Enhanced Inter-Camera Person Re-identification leveraging MixedOrder Relation-Aware Recurrent neural Network (EICPR-MORRNN-TTAO) is proposed. The input images are collected from Market-1501, MSMT17, and Duke MTMC-reID datasets. Afterward, the input image is supplied to pre-processing. In preprocessing, Unsharp Structure Guided Filtering (USGF) is employed to enhance image quality. The pre-processed image is supplied to classification phase for Re-identifying the Inter-Camera Person as Same and Different utilizing Mixed-Order Relation-Aware Recurrent neural Network (MORRNN). Generally, MORRNN does not adopt any optimization methods to determine the ideal parameters to assure accurate person Re-identification. Hence, Triangulation Topology Aggregation Optimizer (TTAO) is proposed to enhance the weight parameters of MORRNN. The EICPR-MORRNN-TTAO method is implemented in Python. The metrics, like Mean Average Precision (MAP), Cumulative Matching Characteristic (CMC), recall, Rank-1, Rank-10, Rank-20, Entropy, error rate, and Receiver Operating Characteristic (ROC) is considered. The EICPR-MORRNN-TTAO method attains 23.10 %, 27.54 % and 25.72 %, higher mAP, 21.48 %, 17.73 %, 25.32 % higher CMC and 20.98 %, 26.66 % and 16.32 % lower Error rate, are compared with existing techniques, like Intra-camera supervised Re-ID (ICSP-RI-PR), Offline-online associated camera-aware proxies for unsupervised Re-ID(OSC-UPRIEIC), and Unsupervised Re-ID with stochastic training strategy (UPRI-EIC-STS) respecti

关键词： Duke MTMC-reID Person Re-Identification Market-1501 MSMT17 Network Triangulation Topology Aggregation Optimizer Unsharp Structure Guided Filtering Mixed-Order Relation-Aware Recurrent neural

来源：评论

学校读者我要写书评

暂无评论

A dense kernel point convolutional neural network for chronic liver disease classification with hybrid chaotic slime mould and giant trevally optimizer

引用

BIOMEDICAL signal processing AND CONTROL 2025年 102卷

作者： Saranya, R. Jaichandran, R. Vinayaka Missions Res Fdn Deemed Be Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor 603104 Tamil Nadu India Vinayaka Missions Res Fdn Deemed Be Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor 603104 Tamil Nadu India

Chronic liver disease affects liver tissues and can lead to liver failure. Early diagnosis is crucial for providing better treatment and reducing the mortality rate. Traditional methods like biopsy and manual analysis of liver computed tomography scan images are commonly used for diagnosis. However, biopsy is invasive and can cause pain and other complications, while manual analysis by a radiologist is time-consuming and requires expert knowledge. This manuscript presents the Dense Kernel Point Convolutional neural Network for Chronic Liver Disease Classification with Hybrid Chaotic Slime Mould and Giant Trevally Optimizer (DKPCNN-CLDC-HybCSMGTO) to improve diagnostic performance. For image segmentation, Fuzzy C-Ordered Means Clustering (IFCMC) method is applied, and for feature extraction, the Intuitionistic Invariant Wavelet Scattering Transform (IWST) is employed. The DKPCNN-CLDC-HybCSM-GTO method is compared with existing methods, including Preoperative Classification of Primary and Metastatic Chronic Liver Disease via Machine Learning-Based Ultrasound Radiomics (PC-PM-LC-MLUR), the Liver Disease Classification from Ultrasound Using Multi-Scale CNN (LDC-USMS), and the Convolutional neural Network for Classifying Primary Chronic Liver Disease Based on Triple-Phase CT and Tumor Marker Information (CNN-CPLC-TPCT-TMI). The evaluation metrics include accuracy, precision, sensitivity, specificity, and F1 score. The DKPCNN-CLDC-HybCSM-GTO approach shows significant improvements over existing methods: 22.36%, 25.42%, and 18.27% higher accuracy;22.36%, 15.42%, and 18.27% higher sensitivity;and 21.36%, 16.42%, and 19.27% higher specificity, respectively. These results demonstrate the method being proposed offers a more a valuable tool for early detection and better treatment outcomes in chronic liver disease.

关键词： Dense kernel point convolutional neural network Hybrid chaotic slime mould and giant trevally optimizer Intuitionistic fuzzy C-ordered means clustering Invariant wavelet scattering transforms

来源：评论

学校读者我要写书评

暂无评论

DEEP neural NETWORK MODEL COMPRESSION AND signal processing 49

DEEP NEURAL NETWORK MODEL COMPRESSION AND SIGNAL PROCESSING

引用

49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ukil, Arijit Majumdar, Angshul Jara, Antonio J. Gama, Joao Tata Consultancy Serv Bengaluru India Indraprastha Inst Informat Technol Delhi India Libelium Zaragoza Spain Univ Porto Porto Portugal

ISBN: (纸本)9798350374520;9798350374513

Deep neural networks (DNN) are used to analyze images, videos, signals and texts require a lot of memory and intensive computing power. For example, the very successful GPT4 model contains more than a few trillion parameters. Although such models are of great impact, but they have been used very little in real-world applications, including industrial Internet of Things, self-driving cars, algorithmic health monitoring for use in limited mobile or edge devices. The requirement to run large models on resource-constrained peripherals has led to significant research interest in compressing DNN models. signal processing researchers have traditionally advocated data (image/video/audio) compression, and by the way, many of these techniques are used for DNN compression. For example, source coding is a basic technique that has been widely used to compress various DNN models. In this paper, we present our views on the use of signal processing methods for DNN model compression.

关键词： Deep neural network signal processing model compression quantization pruning

来源：评论

学校读者我要写书评

暂无评论

FABNet: Frequency-Aware Binarized Network for Single image Super-Resolution

引用

IEEE TRANSACTIONS ON image processing 2023年 32卷 6234-6247页

作者： Jiang, Xinrui Wang, Nannan Xin, Jingwei Li, Keyu Yang, Xi Li, Jie Wang, Xiaoyu Gao, Xinbo Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components. To achieve this, we propose a frequency-aware binarized network (FABNet) for single image super-resolution. First, we leverage the wavelet transformation to decompose the features into low-frequency and high-frequency components and then employ a "divide-and-conquer" strategy to separately process them with well-designed binary network structures. Additionally, we introduce a dynamic binarization process that incorporates learned-threshold binarization during forward propagation and dynamic approximation during backward propagation, effectively addressing the diverse spatial frequency information. Compared to existing methods, our approach is effective in reducing quantization error and recovering image textures. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed methods could surpass state-of-the-art approaches in terms of PSNR and visual quality with significantly reduced computational costs. Our codes are available at https://***/xrjiang527/FABNet-PyTorch.

关键词： Quantization (signal) Superresolution Task analysis neural networks Discrete wavelet transforms image coding Electronic mail Single image super-resolution binary neural network wavelet decomposition lightweight

来源：评论

学校读者我要写书评

暂无评论

Bilateral fusion low-light image enhancement with implicit information constraints

引用

IET image processing 2024年第13期18卷 4141-4150页

作者： Zhu, Jiahui Sang, Shengbo Jian, Aoqun Yang, Le Sang, Luxiao Ge, Yang Kang, Rihui Yang, LiuWei Tao, Lei Hao, RunFang Taiyuan Univ Technol Coll Elect Informat & Opt Engn Shanxi Key Lab Micro Nano Sensors & Artificial Int Taiyuan 030024 Peoples R China Minist Educ Key Lab Adv Transducers & Intelligent Control Syst Taiyuan Peoples R China Shanxi Res Inst 6D Artificial Intelligence Biomed Taiyuan Peoples R China

Research on low-light image enhancement focuses on improving image quality in dim conditions. Recently, deep learning has driven significant advancements, with many studies using neural networks to enhance low-light images. However, most focus on complex network designs to increase nonlinearity, often neglecting the implicit information from local image transformations. This paper introduces an improved U-net-based method for low-light enhancement, retaining the original encoding network and adding branch links in the decoding network. The method uses attention feature fusion to handle image noise and gradients separately, adjusting brightness through a gradient-adaptive transform. This approach optimizes performance using loss functions like peak signal-to-noise ratio and colour consistency. Unlike previous methods, the approach emphasizes extracting implicit information from image gradients, achieving enhancement that aligns with the original brightness distribution. The result is enhanced images with high detail similarity to the original, achieved through end-to-end inference in experiments. This paper introduces a low-light image enhancement technique using bilateral fusion and implicit information constraints. The method employs a dual-pass encoding and decoding network to apply gradient fitting light enhancement transform, controlling both global and local brightness levels. Additionally, the technique combines conventional image evaluation criteria with the perceptual index of super-resolution images to guide the network's learning pathway and stabilize the network, integrating deep learning with traditional visual technology. image

关键词： gradient methods image enhancement image fusion neural net architecture

来源：评论

学校读者我要写书评

暂无评论

BRCycle-GAN: A Near-Infrared Fluorescence image processing Network Based on a Small Training Set

引用

IEEE ACCESS 2024年 12卷 94520-94526页

作者： Wang, Yuran Wang, Lugui Tian, Ye Southern Med Univ Biomat Res Ctr Sch Biomed Engn Guangdong Prov Key Lab Construction & Detect Tissu Guangzhou 510515 Peoples R China

As a noninvasive, nonradiative and high-speed imaging modality, fluorescence imaging in the second near-infrared window (NIR-II, 1,000-1,700 nm) has demonstrated great potential for biomedical research and clinical study. The NIR-II window can be further divided into two spectral regions: NIR-IIa (1,000-1,300 nm) and NIR-IIb (1,500-1,700 nm). Compared to NIR-IIa, imaging in NIR-IIb region affords high-resolution imaging at subcentimeter tissue depths due to suppressed photon scattering and diminished tissue autofluorescence at long wavelengths, but relies on probes with high toxicity. To address the problem, researchers employ deep learning networks to attain NIR-IIb images from NIR-IIa images. However, current methods require numerous paired or unpaired images (more than 2800 images) as training sets, which can hardly acquire. In this work, an innovative convolutional neural network (BRCycle-GAN) is trained based on a small training set (merely 63 images) to transform NIR-IIa images into images with NIR-IIb imaging qualities. The NIR-IIb images generated by BRCycle-GAN outperform previous network models in terms of peak signal-to-noise ratio, cosine similarity and other image evaluation indices.

关键词： Deep learning Optical imaging biomedical image processing optical imaging Deep learning Optical imaging biomedical image processing optical imaging

来源：评论

学校读者我要写书评

暂无评论

REFLECTION REMOVAL USING RECURRENT POLARIZATION-TO-POLARIZATION NETWORK 49

REFLECTION REMOVAL USING RECURRENT POLARIZATION-TO-POLARIZAT...

引用

49th IEEE International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bian, Wenjiao Monno, Yusuke Okutomi, Masatoshi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350344868;9798350344851

This paper addresses reflection removal, which is the task of separating reflection components from a captured image and deriving the image with only transmission components. Considering that the existence of the reflection changes the polarization state of a scene, some existing methods have exploited polarized images for reflection removal. While these methods apply polarized images as the inputs, they predict the reflection and the transmission directly as non-polarized intensity images. In contrast, we propose a polarization-to-polarization approach that applies polarized images as the inputs and predicts "polarized" reflection and transmission images using two sequential networks to facilitate the separation task by utilizing the interrelated polarization information between the reflection and the transmission. We further adopt a recurrent framework, where the predicted reflection and transmission images are used to iteratively refine each other. Experimental results on a public dataset demonstrate that our method outperforms other state-of-the-art methods.

关键词： Reflection Removal Polarization Imaging Recurrent neural Network

来源：评论

学校读者我要写书评

暂无评论

A CONTEXT-ORIENTED MULTI-SCALE neural NETWORK FOR FIRE SEGMENTATION 31

A CONTEXT-ORIENTED MULTI-SCALE NEURAL NETWORK FOR FIRE SEGME...

引用

2024 International Conference on image processing

作者： Zhang, Tony Dick, Robert P. Univ Michigan Elect & Comp Engn Dept Ann Arbor MI 48109 USA

ISBN: (纸本)9798350349405;9798350349399

Existing image-based fire segmentation techniques use convolutional neural networks to handle complicated scenes. Such approaches perform poorly when flame sizes vary greatly and when backgrounds are complex. In this paper, we describe a novel Context-Oriented Multi-Scale Network for fire segmentation. We construct a multi-scale aggregation module that combines semantic information at different levels in the neural network in order to recognize fires with different shapes and sizes. We also describe a Context-Oriented Module, which increases the receptive field of the network by utilizing relationships of all pixels in the feature map in order to obtain features that more effectively discriminate between fire and non-fire pixels. Experimental results demonstrate that our proposed model has a 2.7% higher mean Intersection over Union (mIoU) accuracy than previous fire detection methods.

关键词： Fire segmentation multi-scale context

来源：评论

学校读者我要写书评

暂无评论

Acoustic-to-hyper-spectral: real-time perimeter intrusion detection system monitoring through learnable filters and hyper-spectral image generation from distributed acoustic sensing systems

引用

OPTICS EXPRESS 2025年第3期33卷 4109-4126页

作者： Pierau, Ruth-emely Katsifolis, Jim Meehan, Alaster Rezatofighi, Hamid Stuckey, Peter J. Monash Univ Clayton Vic 3800 Australia Univ Melbourne ARC Training Ctr Optimisat Technol Integrated Meth Parkville Vic 3052 Australia AVA Risk Grp Co Future Fibre Technol 10 Hartnett CL Mulgrave Vic 3170 Australia

This paper presents an integrated distributed acoustic sensing (DAS) system with artificial intelligence to provide real-time system monitoring for fence perimeter and buried system applications. The DAS system is a Rayleigh backscatter based fibre optic sensing system that has been deployed in two real-world, commercial applications to detect acoustic wave propagation and scattering along perimeter lines, and classify intrusions accurately. What we believe to be three novel signal processing methods are proposed to train filters for automatically selecting frequency bands from the power spectrum and generating hyper-spectral images from the data gathered by the DAS system without expert knowledge. The hyper-spectral images are analyzed by a neural network based object detection model. The system achieves 81.8% accuracy on a fence perimeter installation and 60.4% accuracy on a buried system application in detecting and classifying various intrusion events. The evaluation interval of the integrated DAS system framework between event sensing and detection does not exceed 5 s. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Deep learning Hyperspectral imaging Machine learning neural networks signal processing Wave propagation

来源：评论

学校读者我要写书评

暂无评论

An unsupervised multi-focus image fusion method based on Transformer and U-Net

引用

IET image processing 2023年第3期17卷 733-746页

作者： Jin, Xin Xi, Xiuliang Zhou, Ding Ren, Xiaoxuan Yang, Jie Jiang, Qian Yunnan Univ Engn Res Ctr Cyberspace Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China Zunyi Normal Univ Sch Phys & Elect Sci Zunyi Guizhou Peoples R China

This work presents a multi-focus image fusion method based on Transformer and U-Net with an unsupervised training fashion. In this work, the authors introduce Transformer into image fusion because it has great ability to capture the global dependencies and low-frequency features. In image processing, convolutional neural network (CNN) has good performance of detailed feature extraction but a weakness for global feature extraction, and Transformer has limited power in local or detailed information extraction but a strong capacity for global feature extraction. Thus, this work combines the advantages of CNN and Transformer to propose an unsupervised decision map making model for image fusion joint U-Net. The authors construct a model including feature extraction and feature reconstruction modules which correspond to the encoder and decoder network of U-Net, respectively. In addition, perceptual loss is introduced on the basis of structural similarity loss because the combination of these two loss functions can achieve better performance with lower training cost. Experiments show that the proposed image fusion method performs better fusion performance compared with the existing methods.

关键词： feature extraction multifocus image fusion method Sensor fusion Transformer feature reconstruction modules unsupervised learning fusion performance low-frequency features global dependencies neural nets detailed feature extraction Optical, image and video signal processing image fusion Computer vision and image processing techniques convolutional neural nets image fusion joint U-Net deep learning (artificial intelligence) image processing global feature extraction local information extraction Unsupervised learning unsupervised decision map making model detailed information extraction unsupervised training fashion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：