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Benchmarking Object Detection Robustness against Real-World Corruptions Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-15 Jiawei Liu, Zhijie Wang, Lei Ma, Chunrong Fang, Tongtong Bai, Xufan Zhang, Jia Liu, Zhenyu Chen
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Imbalance-Aware Discriminative Clustering for Unsupervised Semantic Segmentation Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-14 Mingyuan Liu, Jicong Zhang, Wei Tang
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PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-14 Haoyu Xie, Changqi Wang, Jian Zhao, Yang Liu, Jun Dan, Chong Fu, Baigui Sun
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A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-14 Fan Ding, Yongyi Zhang, Jiankun Peng, Yuming Ge, Tao Qu, Xingyuan Tao, Jun Chen
The proliferation of mobile phones has generated vast quantities of cellular signaling data (CSD), covering extensive spatial areas and populations. These data, containing spatiotemporal information, can be employed to identify and analyze intercity transport modes, providing valuable insights for understanding travel distribution and behavior. However, CSD are primarily intended for communication
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Adaptive Discriminative Regularization for Visual Classification Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-13 Qingsong Zhao, Yi Wang, Shuguang Dou, Chen Gong, Yin Wang, Cairong Zhao
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Earthquake damage detection and level classification method for wooden houses based on convolutional neural networks and onsite photos Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-13 Kai Wu, Masashi Matsuoka, Haruki Oshio
The results of earthquake damage certification (EDC) surveys are the basis of support measures for improving the lives of disaster victims. To address issues such as a limited workforce to perform EDC surveys and difficulties in judging the level of damage, a damage detection and level classification method for wooden houses using multiple convolutional neural network models is proposed. The proposed
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Exploring the Usage of Pre-trained Features for Stereo Matching Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-11 Jiawei Zhang, Lei Huang, Xiao Bai, Jin Zheng, Lin Gu, Edwin Hancock
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An Empirical Study on Multi-domain Robust Semantic Segmentation Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-10 Yajie Liu, Pu Ge, Qingjie Liu, Shichao Fan, Yunhong Wang
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Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-08 Masato Tamura
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Meet JEANIE: A Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-06 Lei Wang, Jun Liu, Liang Zheng, Tom Gedeon, Piotr Koniusz
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L3AM: Linear Adaptive Additive Angular Margin Loss for Video-Based Hand Gesture Authentication Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-06 Wenwei Song, Wenxiong Kang, Adams Wai-Kin Kong, Yufeng Zhang, Yitao Qiao
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Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-07 Ziyuan Yang, Andrew Beng Jin Teoh, Bob Zhang, Lu Leng, Yi Zhang
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Towards Diverse Binary Segmentation via a Simple yet General Gated Network Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-07 Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, Lei Zhang
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3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-07 Urs Waldmann, Alex Hoi Hang Chan, Hemal Naik, Máté Nagy, Iain D. Couzin, Oliver Deussen, Bastian Goldluecke, Fumihiro Kano
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Designing production planning and control in smart manufacturing Comput. Ind. (IF 10.0) Pub Date : 2024-05-08 Arno Kasper, Martin Land, Will Bertrand, Jacob Wijngaard
To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it
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A lightweight feature attention fusion network for pavement crack segmentation Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-08 Yucheng Huang, Yuchen Liu, Fang Liu, Wei Liu
The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high‐accuracy models are still the main
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Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-08 Jun Su Park, Taehoon Hong, Dong‐Eun Lee, Hyo Seon Park
This study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses
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Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-07 Haiwen Diao, Ying Zhang, Shang Gao, Xiang Ruan, Huchuan Lu
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Construction of design requirements knowledgebase from unstructured design guidelines using natural language processing Comput. Ind. (IF 10.0) Pub Date : 2024-05-07 Baekgyu Kwon, Junho Kim, Hyunoh Lee, Hyo-Won Suh, Duhwan Mun
In the manufacturing industry, unstructured documents such as design guidelines, regulatory documents, and failure cases are essential for product development. However, due to the large volume and frequent revisions of these documents, designers often find it difficult to keep up to date with the latest content. This study presents a method for analyzing the characteristics of unstructured design guidelines
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Displacement sensing based on microscopic vision with high resolution and large measuring range Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-07 Pengfei Wu, Weijie Li, Xuefeng Zhao
Microimage strain sensing (MISS) is a novel piston‐type sensor based on microscopic vision. In this study, optical disc slice is used as information carriers to improve MISS. There are multiple pits on the surface of an optical disc. By using machine vision algorithms, the pits can be converted into digital information, making them scales for recording displacements. By this means, we proposed a sensing
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Tensorized Multi-View Low-Rank Approximation Based Robust Hand-Print Recognition IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-06 Shuping Zhao, Lunke Fei, Bob Zhang, Jie Wen, Pengyang Zhao
Since hand-print recognition, i.e., palmprint, finger-knuckle-print (FKP), and hand-vein, have significant superiority in user convenience and hygiene, it has attracted greater enthusiasm from researchers. Seeking to handle the long-standing interference factors, i.e., noise, rotation, shadow, in hand-print images, multi-view hand-print representation has been proposed to enhance the feature expression
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LEAPSE: Learning Environment Affordances for 3D Human Pose and Shape Estimation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-06 Fangzheng Tian, Sungchan Kim
We live in a 3D world where people interact with each other in the environment. Learning 3D posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner. Existing methods mostly ignore the environment and estimate the
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LSSVC: A Learned Spatially Scalable Video Coding Scheme IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-06 Yifan Bian, Xihua Sheng, Li Li, Dong Liu
Traditional block-based spatially scalable video coding has been studied for over twenty years. While significant advancements have been made, the scope for further improvement in compression performance is limited. Inspired by the success of learned video coding, we propose an end-to-end learned spatially scalable video coding scheme, LSSVC, which provides a new solution for scalable video coding
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Learnable faster kernel-PCA for nonlinear fault detection: Deep autoencoder-based realization J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-05-03 Zelin Ren, Yuchen Jiang, Xuebing Yang, Yongqiang Tang, Wensheng Zhang
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Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-05-03 Fei Yu, Chao Lu, Lvjiang Yin, Jiajun Zhou
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Multi-View Time-Series Hypergraph Neural Network for Action Recognition IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-03 Nan Ma, Zhixuan Wu, Yifan Feng, Cheng Wang, Yue Gao
Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series
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Multi-Stage Image-Language Cross-Generative Fusion Network for Video-Based Referring Expression Comprehension IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-02 Yujia Zhang, Qianzhong Li, Yi Pan, Xiaoguang Zhao, Min Tan
Video-based referring expression comprehension is a challenging task that requires locating the referred object in each video frame of a given video. While many existing approaches treat this task as an object-tracking problem, their performance is heavily reliant on the quality of the tracking templates. Furthermore, when there is not enough annotation data to assist in template selection, the tracking
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Relationship Learning From Multisource Images via Spatial-Spectral Perception Network IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-02 Yunhao Gao, Wei Li, Junjie Wang, Mengmeng Zhang, Ran Tao
Advances in multisource remote sensing have allowed for the development of more comprehensive observation. The adoption of deep convolutional neural networks (CNN) naturally includes spatial-spectral information, which has achieved promising performance in multisource data classification. However, challenges are still found with the extraction of spatial distribution and spectrum relationships, which
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Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-02 Mauricio Pereira, Antonio Maria D'Altri, Stefano de Miranda, Branko Glisic
In this paper, we present a method based on an ensemble of convolutional neural networks (CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls using as only input the crack pattern. We use an accurate block‐based numerical model to generate mechanically consistent crack patterns induced by external actions (earthquake‐like loads and differential settlements). For
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A causal discovery approach to study key mixed traffic‐related factors and age of highway affecting raveling Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-02 Zili Wang, Panchamy Krishnakumari, Kumar Anupam, Hans van Lint, Sandra Erkens
The relationship between real‐world traffic and pavement raveling is unclear and subject to ongoing debates. This research proposes a novel approach that extends beyond traditional correlation analyses to explore causal mechanisms between mixed traffic and raveling. This approach incorporates the causal discovery method, and is applied to five Dutch porous asphalt (PA) highway sites that have substantial
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Cover Image, Volume 39, Issue 10 Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-05-02
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Scaling Up Multi-domain Semantic Segmentation with Sentence Embeddings Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-05-01 Wei Yin, Yifan Liu, Chunhua Shen, Baichuan Sun, Anton van den Hengel
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Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-05-01 Baoliang Chen, Hanwei Zhu, Lingyu Zhu, Shiqi Wang, Sam Kwong
The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can
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A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples Comput. Ind. (IF 10.0) Pub Date : 2024-05-01 Zhenya Wang, Qiusheng Luo, Hui Chen, Jingshan Zhao, Ligang Yao, Jun Zhang, Fulei Chu
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A Causal Inspired Early-Branching Structure for Domain Generalization Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-30 Liang Chen, Yong Zhang, Yibing Song, Zhen Zhang, Lingqiao Liu
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Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-30 Ekaterina Nepovinnykh, Ilia Chelak, Tuomas Eerola, Veikka Immonen, Heikki Kälviäinen, Maksim Kholiavchenko, Charles V. Stewart
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A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-04-30 Jiun-Shiung Lin, Kun-Huang Chen
Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality
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Design ontology for cognitive thread supporting traceability management in model-based systems engineering J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-04-30 Shouxuan Wu, Guoxin Wang, Jinzhi Lu, Zhenchao Hu, Yan Yan, Dimitris Kiritsis
Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between
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Occlusion-Aware Transformer With Second-Order Attention for Person Re-Identification IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-30 Yanping Li, Yizhang Liu, Hongyun Zhang, Cairong Zhao, Zhihua Wei, Duoqian Miao
Person re-identification (ReID) typically encounters varying degrees of occlusion in real-world scenarios. While previous methods have addressed this using handcrafted partitions or external cues, they often compromise semantic information or increase network complexity. In this paper, we propose a new method from a novel perspective, termed as OAT. Specifically, we first use a Transformer backbone
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QueryTrack: Joint-Modality Query Fusion Network for RGBT Tracking IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-30 Huijie Fan, Zhencheng Yu, Qiang Wang, Baojie Fan, Yandong Tang
Existing RGB-Thermal trackers usually treat intra-modal feature extraction and inter-modal feature fusion as two separate processes, therefore the mutual promotion of extraction and fusion is neglected. Then, the complementary advantages of RGB-T fusion are not fully exploited, and the independent feature extraction is not adaptive to modal quality fluctuation during tracking. To address the limitations
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Learning to Recover Spectral Reflectance From RGB Images IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-30 Dong Huo, Jian Wang, Yiming Qian, Yee-Hong Yang
This paper tackles spectral reflectance recovery (SRR) from RGB images. Since capturing ground-truth spectral reflectance and camera spectral sensitivity are challenging and costly, most existing approaches are trained on synthetic images and utilize the same parameters for all unseen testing images, which are suboptimal especially when the trained models are tested on real images because they never
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Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-30 Liuxin Bao, Xiaofei Zhou, Xiankai Lu, Yaoqi Sun, Haibing Yin, Zhenghui Hu, Jiyong Zhang, Chenggang Yan
Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers
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A self-supervised leak detection method for natural gas gathering pipelines considering unlabeled multi-class non-leak data Comput. Ind. (IF 10.0) Pub Date : 2024-04-30 Zhonglin Zuo, Hao Zhang, Zheng Li, Li Ma, Shan Liang, Tong Liu, Mehmet Mercangöz
Detecting leaks in natural gas gathering pipelines is paramount for the safe and reliable operation of the gas and oil industry. Due to the lack of leak data and the changes in leak features, semi-supervised leak detection methods that use normal data for health model learning have attracted much attention. However, these approaches usually consider one-class normal samples as health data, which may
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Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling Comput. Ind. (IF 10.0) Pub Date : 2024-04-30 Chang Su, Yong Han, Xin Tang, Qi Jiang, Tao Wang, Qingchen He
The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core
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A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-30 Yuxiao Zhang, Jin Shi, Shehui Tan, Yingjie Wang
Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category
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A dynamic graph deep learning model with multivariate empirical mode decomposition for network‐wide metro passenger flow prediction Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-30 Hao Huang, Jiannan Mao, Leilei Kang, Weike Lu, Sijia Zhang, Lan Liu
Network‐wide short‐term passenger flow prediction is critical for the operation and management of metro systems. However, it is challenging due to the inherent non‐stationarity, nonlinearity, and spatial–temporal dependencies within passenger flow. To tackle these challenges, this paper introduces a hybrid model called multi‐scale dynamic propagation spatial–temporal network (MSDPSTN). Specifically
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Matching Compound Prototypes for Few-Shot Action Recognition Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-29 Yifei Huang, Lijin Yang, Guo Chen, Hongjie Zhang, Feng Lu, Yoichi Sato
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Anisotropic Scale-Invariant Ellipse Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-04-29 Zikai Wang, Baojiang Zhong, Kai-Kuang Ma
Detecting ellipses poses a challenging low-level task indispensable to many image analysis applications. Existing ellipse detection methods commonly encounter two fundamental issues. First, inferior detection accuracy could be incurred on a small ellipse than that on a large one; this introduces the scale issue. Second, inferior detection accuracy could be yielded along the minor axis than along the
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Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-27 Xinyue Huo, Lingxi Xie, Hengtong Hu, Wengang Zhou, Houqiang Li, Qi Tian
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Estimating and explaining regional land value distribution using attention-enhanced deep generative models Comput. Ind. (IF 10.0) Pub Date : 2024-04-27 Feifeng Jiang, Jun Ma, Christopher John Webster, Weiwei Chen, Wei Wang
Accurate land valuation is crucial in sustainable urban development, influencing pivotal decisions on resource allocation and land-use strategies. Most existing studies, primarily using point-based modeling approaches, face challenges on granularity, generalizability, and spatial effect capturing, limiting their effectiveness in regional land valuation with high granularity. This study therefore proposes
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Light Flickering Guided Reflection Removal Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-04-26 Yuchen Hong, Yakun Chang, Jinxiu Liang, Lei Ma, Tiejun Huang, Boxin Shi
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EA-GAT: Event aware graph attention network on cyber-physical systems Comput. Ind. (IF 10.0) Pub Date : 2024-04-26 Mehmet Yavuz Yağci, Muhammed Ali Aydin
Anomaly detection with high accuracy, recall, and low error rate is critical for the safe and uninterrupted operation of cyber-physical systems. However, detecting anomalies in multimodal time series with different modalities obtained from cyber-physical systems is challenging. Although deep learning methods show very good results in anomaly detection, they fail to detect anomalies according to the
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Component‐level point cloud completion of bridge structures using deep learning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Gen Matono, Mayuko Nishio
Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three‐dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component‐wise
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Aggregation formulation for on‐site multidepot vehicle scheduling scenario Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Yi Gao, Yuanjie Tang, Rengkui Liu
The multidepot vehicle scheduling problem (MDVSP) is a fundamental public transport challenge. To address the large‐scale model and inherent solution symmetry associated with the traditional trip‐to‐trip connection‐based approach for MDVSP, a new trip‐to‐route (T2R) connection‐based approach is proposed. Considering real‐world problem characteristics with numerous trips sharing common origin–destination
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Rapid pedestrian‐level wind field prediction for early‐stage design using Pareto‐optimized convolutional neural networks Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Alfredo Vicente Clemente, Knut Erik Teigen Giljarhus, Luca Oggiano, Massimiliano Ruocco
Traditional computational fluid dynamics (CFD) methods used for wind field prediction can be time‐consuming, limiting architectural creativity in the early‐stage design process. Deep learning models have the potential to significantly speed up wind field prediction. This work introduces a convolutional neural network (CNN) approach based on the U‐Net architecture, to rapidly predict wind in simplified
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Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-04-26 Tianyong Jiang, Lingyun Li, Bijan Samali, Yang Yu, Ke Huang, Wanli Yan, Lei Wang
To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight
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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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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
With human action anticipation becoming an essential tool for many practical applications, there has been an increasing trend in developing more accurate anticipation models in recent years. Most of the existing methods target standard action anticipation datasets, in which they could produce promising results by learning action-level contextual patterns. However, the over-simplified scenarios of standard
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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
Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level