Article
Chemistry, Analytical
Sally Ghanem, Ryan A. Kerekes, Ryan Tokola
Summary: In this study, a framework for decision fusion utilizing features extracted from vehicle images and their detected wheels is proposed, which shows improved matching accuracy by considering decisions made by whole-vehicle or wheels-only matching networks.
Article
Computer Science, Information Systems
Kamel Aizi, Mohamed Ouslim
Summary: This paper presents a new multibiometric fusion method for individual identification using the iris and fingerprint modalities. Test results show that the proposed fusion methods outperform single modality methods, with the BCC fusion approach slightly outperforming BFL.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
AmirHossein Nafei, Amir Javadpour, Hadi Nasseri, Wenjun Yuan
Summary: The paper introduces neutrosophic set theory and its application in decision-making, including a modified score function for ranking single-valued neutrosophic numbers and a TOPSIS method based on this function. Empirical experiments validate the effectiveness and rationality of the method in decision-making.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Bo Sun, Ganchao Liu
Summary: In this paper, a dual attention and dual fusion scene matching algorithm is proposed for visual geo-localization of UAV images with satellite maps. The algorithm utilizes a siamese network for accurate matching, a dual-attention model for improved semantic feature extraction, and a dual fusion model for enhanced matching confidence. Experimental results on LA850 and NWPU-ChangAn datasets demonstrate the algorithm's efficiency compared to other methods.
Article
Environmental Sciences
Zhongfei Chen, Xiaoyu Zhang, Fanglin Chen
Summary: The study found that driving restrictions in China did not significantly improve air quality in cities, as people did not clearly shift from private cars to public transportation. Additionally, the effects of driving restrictions varied across different regions and cities in China.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Engineering, Industrial
Na Yuan, Haiming Liang, Min Huang, Qing Wang
Summary: As a supply chain integrator, a fourth party logistics (4PL) typically does not have its own logistics facilities, so the 4PL needs to match third party logistics (3PLs) and customers to meet customers' logistics service demands. A novel two-sided logistics matching method is proposed to consider the trading psychology and matching effort, aiming to improve supply chain management efficiency and establish long-term stable cooperative relationships with customers and 3PLs.
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS
(2023)
Article
Environmental Sciences
Anbang Liang, Qingquan Li, Zhipeng Chen, Dejin Zhang, Jiasong Zhu, Jianwei Yu, Xu Fang
Summary: This study introduces an IMU-assisted fisheye image matching method called So-RANSAC, which improves mismatch removal accuracy by converting putative correspondences into fisheye spherical coordinates and utilizing IMU to provide relative rotation angles. Experimental results demonstrate that So-RANSAC outperforms commonly used fisheye image matching methods in various experimental scenarios.
Article
Computer Science, Information Systems
Diwakar Agarwal, Atul Bansal
Summary: Latent fingerprint identification, the most prevalent process used by the forensic community, is improved by proposing an algorithm that utilizes pores in addition to minutiae for matching. Experimental results demonstrate that the fusion of pores and minutiae significantly enhances the latent fingerprint recognition rate.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Analytical
Lilan Liu, Xiang Wan, Jiaying Li, Wenxi Wang, Zenggui Gao
Summary: This paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system and verifies its effectiveness through experiments.
Article
Computer Science, Information Systems
Xueyang Qin, Lishuang Li, Guangyao Pang
Summary: This paper proposes a Multi-Scale Motivated Neural Network (MSMNN) model for image-text matching. The model extracts visual and textual features from three scales and utilizes a cross-modal interaction module to discover the potential relationship between image-text pairs. Furthermore, a matching score fusion algorithm is proposed to fuse matching results from three different levels. Extensive experiments show the effectiveness of the method, achieving competitive results on two well-known datasets.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
A. Suresh, V. Rajaram, V. Anbarasu, N. Deepa
Summary: The innovative image preprocessing and fusion techniques are developed based on multiresolution-based fusion techniques. The proposed work involves three-level decomposition in image preprocessing and optimized multi-feature fuzzy clustering (OMFC) for feature extraction, fusion, and image reconstruction. The source images are decomposed into smooth, structure, and boundary levels using spatial domain. The large and small frequency bands are merged using enhanced parametric adaptive pulse coupled neural network technique for preserving significant information and data extraction. Reconstruction of the target image is performed using inverse OMFC, and validations are done in various categories. The demonstration of the proposed technique shows better performance compared to existing techniques.
Article
Chemistry, Analytical
Lian Wu, Yong Xu, Zhongwei Cui, Yu Zuo, Shuping Zhao, Lunke Fei
Summary: This paper proposes a heuristic palmprint recognition method by extracting triple types of features without requiring any training samples. Experimental results show the promising effectiveness of the proposed method on three widely used palmprint databases.
Article
Oncology
Debashis Ghosh, Arya Amini, Bernard L. Jones, Sana D. Karam
Summary: The exclusion of unmatched observations in propensity score matching affects the generalizability of causal effects. Machine learning methods can help identify the differences between the study population and the unmatched subpopulation.
FRONTIERS IN ONCOLOGY
(2022)
Article
Environmental Sciences
Shenfu Zhang, Xiangchao Meng, Qiang Liu, Gang Yang, Weiwei Sun
Summary: In this paper, a novel feature-decision level collaborative fusion network (FDCFNet) is proposed for hyperspectral and LiDAR classification. A multilevel interactive fusion module is used to indirectly connect hyperspectral and LiDAR flows to refine the spectral-elevation information. The fusion features of the intermediate branch further enhance the shared-complementary information of hyperspectral and LiDAR to reduce the modality differences. Experiments on three public benchmark datasets demonstrate the effectiveness of the proposed methods.
Article
Oncology
Gaoyuan Wang, Chenglong Huang, Kaibin Yang, Rui Guo, Youyu Qiu, Wenfei Li, Yanping Mao, Linglong Tang, Jun Ma
Summary: This study evaluated the feasibility of sparing level Ib irradiation in NPC patients, showing that neck level Ib-sparing appears to be safe and feasible in specific conditions, and can reduce dry mouth symptoms.
RADIOTHERAPY AND ONCOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)