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Accurate 3D Measurement of Complex Texture Objects by Height Compensation Using a Dual-Projector Structure IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-22 Pengcheng Yao, Yuchong Chen, Shaoyan Gai, Feipeng Da
Fringe projection profilometry is a widely used technique for 3D measurement due to its high accuracy and speed. However, the accuracy significantly decreases when measuring complex texture objects, especially in the junction of different colors. This paper analyzes the causes of errors resulting from complex textures and proposes a height compensation method to revise the error by employing a dual-projector
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PIE: Physics-Inspired Low-Light Enhancement Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-25 Dong Liang, Zhengyan Xu, Ling Li, Mingqiang Wei, Songcan Chen
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I2DFormer+: Learning Image to Document Summary Attention for Zero-Shot Image Classification Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool, Federico Tombari
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Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Yu Wang, Xinjie Yao, Pengfei Zhu, Weihao Li, Meng Cao, Qinghua Hu
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WildCLIP: Scene and Animal Attribute Retrieval from Camera Trap Data with Domain-Adapted Vision-Language Models Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Valentin Gabeff, Marc Rußwurm, Devis Tuia, Alexander Mathis
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An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Lei Zhang, Xiaowei Fu, Fuxiang Huang, Yi Yang, Xinbo Gao
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Position, Padding and Predictions: A Deeper Look at Position Information in CNNs Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Md Amirul Islam, Matthew Kowal, Sen Jia, Konstantinos G. Derpanis, Neil D. B. Bruce
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Descriptor Distillation: A Teacher-Student-Regularized Framework for Learning Local Descriptors Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Yuzhen Liu, Qiulei Dong
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MutualFormer: Multi-modal Representation Learning via Cross-Diffusion Attention Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-24 Xixi Wang, Xiao Wang, Bo Jiang, Jin Tang, Bin Luo
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Multi-Label Action Anticipation for Real-World Videos with Scene Understanding IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-25 Yuqi Zhang, Xiucheng Li, Hao Xie, Weijun Zhuang, Shihui Guo, Zhijun Li
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Fine-grained Recognition with Learnable Semantic Data Augmentation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-25 Yifan Pu, Yizeng Han, Yulin Wang, Junlan Feng, Chao Deng, Gao Huang
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Multistage charging facility planning on the expressway coordinated with the power structure transformation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-25 Tian‐yu Zhang, En‐jian Yao, Yang Yang, Hong‐Ming Yang, Dong‐bo Guo, David Z. W. Wang
This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits
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Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-23 Elisa Warner, Joonsang Lee, William Hsu, Tanveer Syeda-Mahmood, Charles E. Kahn Jr., Olivier Gevaert, Arvind Rao
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VNAS: Variational Neural Architecture Search Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-23 Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao
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Augmenting the Softmax with Additional Confidence Scores for Improved Selective Classification with Out-of-Distribution Data Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-23 Guoxuan Xia, Christos-Savvas Bouganis
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Mitigating Search Interference with Task-Aware Nested Search IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Jiho Lee, Eunwoo Kim
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CS2DIPs: Unsupervised HSI Super-Resolution Using Coupled Spatial and Spectral DIPs IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Yuan Fang, Yipeng Liu, Chong-Yung Chi, Zhen Long, Ce Zhu
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Multi-Stage Network with Geometric Semantic Attention for Two-View Correspondence Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Shuyuan Lin, Xiao Chen, Guobao Xiao, Hanzi Wang, Feiran Huang, Jian Weng
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Model-based Explainable Deep Learning for Light-field Microscopy Imaging IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Pingfan Song, Herman Verinaz Jadan, Carmel L. Howe, Amanda J. Foust, Pier Luigi Dragotti
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Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Wenhao Shen, Mingliang Zhou, Jun Luo, Zhengguo Li, Sam Kwong
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Learning Contrast-enhanced Shape-biased Representations for Infrared Small Target Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Fanzhao Lin, Kexin Bao, Yong Li, Dan Zeng, Shiming Ge
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Fine-Grained Essential Tensor Learning for Robust Multi-View Spectral Clustering IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Chong Peng, Kehan Kang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng
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Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-24 Ting Yu, Kunhao Fu, Jian Zhang, Qingming Huang, Jun Yu
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An intelligent optimization method for the facility environment on rural roads Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-24 Weixi Ren, Bo Yu, Yuren Chen, Kun Gao, Shan Bao, Zhixuan Wang, Yuting Qin
This study develops an intelligent optimization method of the facility environment (i.e., road facilities and surrounding landscapes) from drivers’ visual perception to adjust operation speeds on rural roads. Different from previous methods that heavily rely on expert experience and are time‐consuming, this method can rapidly generate optimized visual images of the facility environment and promptly
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Virtual trial assembly of large steel members with bolted connections based on multiscale point cloud fusion Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-24 Zeyu Zhang, Dong Liang, Haibin Huang, Lu Sun
Virtual trial assembly (VTA) using 3D laser scanning as the digital carrier can overcome the shortcomings of time‐consuming and costly physical preassembly. However, its application in large steel structures with bolted connections remains limited. First, this study introduces a novel approach for acquiring multiscale point cloud data of large steel members using terrestrial laser scanners (TLSs) and
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Exploring Video Denoising in Thermal Infrared Imaging: Physics-inspired Noise Generator, Dataset and Model IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-23 Lijing Cai, Xiangyu Dong, Kailai Zhou, Xun Cao
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Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-21 M. Azimi, T. Y. Yang
High‐resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages. However, conventional convolutional neural network‐based segmentation methods face limitations in real‐world deployment, particularly when handling high‐resolution images producing low‐resolution outputs. This study introduces a
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A blockchain-based deployment framework for protecting building design intellectual property rights in collaborative digital environments Comput. Ind. (IF 10.0) Pub Date : 2024-04-20 Weisheng Lu, Liupengfei Wu
Protecting intellectual property rights (IPR) in the architecture, engineering, and construction (AEC) industry is a long-standing challenge. In the collaborative digital environments, where multiple professionals use digital platforms such as building information modelling to collaborate on a building design, this challenge has intensified. This research harnesses the functions of blockchain technology
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On Finite Difference Jacobian Computation in Deformable Image Registration Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-18 Yihao Liu, Junyu Chen, Shuwen Wei, Aaron Carass, Jerry Prince
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Classification of Small Drones Using Low-Uncertainty Micro-Doppler Signature Images and Ultra-Lightweight Convolutional Neural Network IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-19 Junhyeong Park, Jun-Sung Park
Many studies have attempted to classify small drones in response to threats posed by the technical progress of small drones. Recently, small drones have been classified utilizing convolutional neural networks (CNNs) with micro-Doppler signature (MDS) images generated from frequency-modulated continuous-wave (FMCW) radars. This study proposes a comprehensive method for classifying small drones in real-time
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Image Reconstruction for Accelerated MR Scan With Faster Fourier Convolutional Neural Networks IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-19 Xiaohan Liu, Yanwei Pang, Xuebin Sun, Yiming Liu, Yonghong Hou, Zhenchang Wang, Xuelong Li
High quality image reconstruction from undersampled ${k}$ -space data is key to accelerating MR scanning. Current deep learning methods are limited by the small receptive fields in reconstruction networks, which restrict the exploitation of long-range information, and impede the mitigation of full-image artifacts, particularly in 3D reconstruction tasks. Additionally, the substantial computational
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Fast Continual Multi-View Clustering With Incomplete Views IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-19 Xinhang Wan, Bin Xiao, Xinwang Liu, Jiyuan Liu, Weixuan Liang, En Zhu
Multi-view clustering (MVC) has attracted broad attention due to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). Specifically, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated
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Material augmented semantic segmentation of point clouds for building elements Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-17 Houhao Liang, Justin K. W. Yeoh, David K. H. Chua
Point clouds are utilized to enable automated engineering applications for their ability to represent spatial geometry. However, they inherently lack detailed surface textures, posing challenges in differentiating objects at the texture level. Hence, this study introduces a 2D–3D fusing approach, leveraging material properties recognized from registered images as an augmented feature to enhance deep
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Multi-Relational Deep Hashing for Cross-Modal Search IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-16 Xiao Liang, Erkun Yang, Yanhua Yang, Cheng Deng
Deep cross-modal hashing retrieval has recently made significant progress. However, existing methods generally learn hash functions with pairwise or triplet supervisions, which involves learning the relevant information by splicing partial similarity between data pairs; notably, this approach only captures the data similarity locally and incompletely, resulting in sub-optimal retrieval performance
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An efficient Bayesian method with intrusive homotopy surrogate model for stochastic model updating Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-16 Hui Chen, Bin Huang, Heng Zhang, Kaiyi Xue, Ming Sun, Zhifeng Wu
This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. Then combining the advanced delayed‐rejection adaptive Metropolis–Hastings sampling technology with HSM, the structural FE model can be updated by uncertain measurement
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Integrated corridor management by cooperative traffic signal and ramp metering control Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-16 Abdullah Al Farabi, Rasool Mohebifard, Ramin Niroumand, Ali Hajbabaie, Mohammed Hadi, Lily Elefteriadou
This paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor.
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GLPanoDepth: Global-to-Local Panoramic Depth Estimation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-15 Jiayang Bai, Haoyu Qin, Shuichang Lai, Jie Guo, Yanwen Guo
Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete
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Traffic prediction via clustering and deep transfer learning with limited data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-15 Xiexin Zou, Edward Chung
This paper proposes a method based on the clustering algorithm, deep learning, and transfer learning (TL) for short‐term traffic prediction with limited data. To address the challenges posed by limited data and the complex and diverse traffic patterns observed in traffic networks, we propose a profile model based on few‐shot learning to extract each detector's unique profiles. These profiles are then
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A lightweight Transformer‐based neural network for large‐scale masonry arch bridge point cloud segmentation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-15 Yixiong Jing, Brian Sheil, Sinan Acikgoz
Transformer architecture based on the attention mechanism achieves impressive results in natural language processing (NLP) tasks. This paper transfers the successful experience to a 3D point cloud segmentation task. Inspired by newly proposed 3D Transformer neural networks, this paper introduces a new Transformer‐based module, which is called Local Geo‐Transformer. To alleviate the heavy memory consumption
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Learning with Noisy Correspondence Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-13 Zhenyu Huang, Peng Hu, Guocheng Niu, Xinyan Xiao, Jiancheng Lv, Xi Peng
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Ensemble Quadratic Assignment Network for Graph Matching Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-13 Haoru Tan, Chuang Wang, Sitong Wu, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
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ISTR: Mask-Embedding-Based Instance Segmentation Transformer IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-12 Jie Hu, Yao Lu, Shengchuan Zhang, Liujuan Cao
Transformer-based instance-level recognition has attracted increasing research attention recently due to the superior performance. However, although attempts have been made to encode masks as embeddings into Transformer-based frameworks, how to combine mask embeddings and spatial information for a transformer-based approach is still not fully explored. In this paper, we revisit the design of mask-embedding-based
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Deep Variation Prior: Joint Image Denoising and Noise Variance Estimation Without Clean Data IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-12 Rihuan Ke
With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth data for training. The strong data requirement can be mitigated by unsupervised learning techniques, however, accurate modelling of images or noise variances
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Saliency Guided Deep Neural Network for Color Transfer With Light Optimization IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-12 Yuming Fang, Pengwei Yuan, Chenlei Lv, Chen Peng, Jiebin Yan, Weisi Lin
Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization
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Data‐driven out‐of‐order model for synchronized planning, scheduling, and execution in modular construction fit‐out management Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-12 Yishuo Jiang, Mingxing Li, Benedict Jun Ma, Ray Y. Zhong, George Q. Huang
Fit‐out operations in modular construction exhibit unique features, such as limited room space and diversly distributed operations in the building. These features pose significant challenges to planning, scheduling, and execution (PSE) of fit‐out activities due to operational parallelism, distributional diversity, and narrower constrained time window in modular construction. Hence, logistics‐operation
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Estimation of load for tunnel lining in elastic soil using physics‐informed neural network Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-11 G. Wang, Q. Fang, J. Wang, Q. M. Li, J. Y. Chen, Y. Liu
A reverse calculation method termed soil and lining physics‐informed neural network (SL‐PINN) is proposed for the estimation of load for tunnel lining in elastic soil based on radial displacement measurements of the tunnel lining. To achieve efficient and accurate calculations, the framework of SL‐PINN is specially designed to consider the respective displacement characteristics of surrounding soil
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Single Stage Adaptive Multi-Attention Network for Image Restoration IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-10 Anas Zafar, Danyal Aftab, Rizwan Qureshi, Xinqi Fan, Pingjun Chen, Jia Wu, Hazrat Ali, Shah Nawaz, Sheheryar Khan, Mubarak Shah
Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally
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High-Quality and Diverse Few-Shot Image Generation via Masked Discrimination IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-10 Jingyuan Zhu, Huimin Ma, Jiansheng Chen, Jian Yuan
Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained on only a few images. The discriminator can easily remember all the training samples and guide the generator to replicate them, leading to severe diversity degradation. Several methods have been proposed to relieve overfitting
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RefQSR: Reference-Based Quantization for Image Super-Resolution Networks IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-10 Hongjae Lee, Jun-Sang Yoo, Seung-Won Jung
Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied
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Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-10 Wenjin Qin, Hailin Wang, Feng Zhang, Weijun Ma, Jianjun Wang, Tingwen Huang
Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to
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Smartphone‐based method for measuring maximum peak tensile and compressive strain Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-10 Xixian Chen, Huan Li, Chenhao Zhao, Guangyi Zhou, Weijie Li, Xue Zhang, Xuefeng Zhao
This paper proposes an innovative smartphone‐based strain sensing method (named MaxCpture) for measuring maximum peak tensile and compressive strains. The MaxCpture method is able to record the maximum peak strain of a structure without continuous power supply and real‐time monitoring. This method combines the maximum peak strain sensor, a smartphone, and the microimage sensing algorithm. Crucially
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Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-08 Yuhang Li, Shikuang Deng, Xin Dong, Shi Gu
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Source-Guided Target Feature Reconstruction for Cross-Domain Classification and Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-09 Yifan Jiao, Hantao Yao, Bing-Kun Bao, Changsheng Xu
Existing cross-domain classification and detection methods usually apply a consistency constraint between the target sample and its self-augmentation for unsupervised learning without considering the essential source knowledge. In this paper, we propose a Source-guided Target Feature Reconstruction (STFR) module for cross-domain visual tasks, which applies source visual words to reconstruct the target
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CRetinex: A Progressive Color-Shift Aware Retinex Model for Low-Light Image Enhancement Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-08 Han Xu, Hao Zhang, Xunpeng Yi, Jiayi Ma
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An advanced cooperative multi-hive drone swarm system for global dynamic multi-source information awareness J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-04-08 Jinkun Men, Chunmeng Zhao
With the advancement of unmanned aerial vehicle technology, dynamic monitoring with drones has been widely adopted to enhance multi-source information awareness capabilities. The cooperative strategy among drones still poses a significant challenge. Redundant actions within the drone swarm system can lead to a noticeable decrease in awareness performance. In this work, an advanced cooperative multi-hive
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Relationship-Incremental Scene Graph Generation by a Divide-and-Conquer Pipeline with Feature Adapter IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-08 Xuewei Li, Guangcong Zheng, Yunlong Yu, Naye Ji, Xi Li
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Context‐aware hand gesture interaction for human–robot collaboration in construction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-08 Xin Wang, Dharmaraj Veeramani, Fei Dai, Zhenhua Zhu
Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the
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An integrated temporal and spatial synchronization for two-echelon vehicle routing problem in waste collection system J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-04-06 Golman Rahmanifar, Mostafa Mohammadi, Mostafa Hajiaghaei-Keshteli, Gaetano Fusco, Chiara Colombaroni
This paper presents a two-echelon vehicle routing problem (2E-VRP) with vehicle synchronization at meeting points for the reverse logistic network to collect waste in the urban area. Low-capacity vehicles are utilized to perform collection only in the inner part of the city because of restricted access and limited infrastructure to be expanded. While, high-capacity vehicles are used to transform waste