Article
Computer Science, Software Engineering
Yuanfeng Lian, Dingru Gu, Jing Hua
Summary: This paper presents a novel and efficient shape correspondence network, SORCNet, for non-rigid 3D shape analysis. By learning point features based on enhanced descriptors, SORCNet solves dense correspondence between non-rigid shapes. The optimized residual network structure and shared capsule network structure are designed to improve the efficiency and accuracy of the model. Experimental results on public datasets demonstrate the superior effectiveness, accuracy, and adaptability of the proposed method in 3D shape correspondence.
Article
Computer Science, Software Engineering
Yusuf Sahillioglu, Devin Horsman
Summary: This paper proposes a fully-automatic method to compute point-to-point dense correspondences between isometric shapes with topological noise. It utilizes fuzzy votes based on topologically-robust heat diffusion and introduces reodesics to make the matching more stable to topological noise. Experimental results demonstrate its advantages over state-of-the-art methods on multiple datasets.
ACM TRANSACTIONS ON GRAPHICS
(2023)
Article
Chemistry, Multidisciplinary
Shiran Ziv Sharabani, Nicole Edelstein-Pardo, Maya Molco, Netanel Bachar Schwartz, Michael Morami, Aya Sivan, Yonatan Gendelman Rom, Roey Evental, Eli Flaxer, Amit Sitt
Summary: This study presents a method for constructing highly-ordered 2D networks using thermoresponsive mesoscale polymeric fibers, and demonstrates that the morphing of these networks depends on the physical attributes of the fibers. By changing the fiber diameter and mesh size, the morphology of the network can be controlled to achieve shape-morphing. This hierarchically induced phase transition offers a new approach for shape control of synthetic materials.
ADVANCED FUNCTIONAL MATERIALS
(2022)
Article
Computer Science, Software Engineering
Mikhail Panine, Maxime Kirgo, Maks Ovsjanikov
Summary: The paper proposes a principled approach for non-isometric landmark-preserving non-rigid shape matching using the functional map framework. They introduce a novel landmark-adapted basis and formulate a conformally-invariant energy to promote high-quality landmark-preserving maps.
COMPUTER GRAPHICS FORUM
(2022)
Article
Engineering, Biomedical
Laidi Amel, Mohammed Ammar, Mostafa El Habib Daho, Said Mahmoudi
Summary: This work aims to develop an automatic detection process for cardiac structures in both short-axis and long-axis views. Inspired by the human thinking process, a workflow is designed to enhance explainability. Separation of images into two classes using a Residual Network model and general segmentation using Particle Swarm Optimization are performed. Several shape descriptors and ANOVA are used for region of interest detection. The proposed method achieves a high accuracy in separating views and detecting cardiac structures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Software Engineering
Shaolong Liu, Xingce Wang, Xiangyuan Liu, Zhongke Wu, Hock Soon Seah
Summary: This study presents an effective spectral matching method based on a shape association graph for finding region correspondences between cel animation keyframes. The method formulates the correspondence problem as a quadratic assignment problem that considers both the geometric and topological features of regions to find the globally optimal correspondence.
COMPUTATIONAL VISUAL MEDIA
(2023)
Article
Engineering, Electrical & Electronic
Juhani Virtanen, Maria Koivisto, Tarja Toimela, Antti Vehkaoja, Tuula Heinonen, Sampo Tuukkanen
Summary: In this study, a technique for dual axis contraction force measurement of human cell based cardiac tissue constructs was presented. The system utilized in-house prepared force sensors and pattern matching method to accurately measure the peak contraction force and detect anomalies in cardiac contraction cycles. The results demonstrated reliable measurements and low relative standard deviation in peak contraction force, showcasing the effectiveness of the proposed dual axis force measurement system.
IEEE SENSORS JOURNAL
(2021)
Article
Geosciences, Multidisciplinary
Hepeng Zheng, Yun Zhang, Haoran Li, Zuhang Wu, Zeming Zhou
Summary: This study finds that there is an overestimation of axis ratios (ARs) for raindrops when using 2D images for parameterization, especially for large raindrops and 2DVD raw products. This overestimation leads to an underestimation of differential reflectivity (Z(DR)) in simulated data and results in an underestimation of more than 20% for heavy rain rate estimation using polarimetric radar. In precipitating systems with drastic changes in raindrop size distribution, it is important to consider more realistic ARs.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Engineering, Biomedical
Ya-nan Fu, Yongsan Li, Bo Deng, Yingjie Yu, Fang Liu, Lei Wang, Guang Chen, Lei Tao, Yen Wei, Xing Wang
Summary: A spatiotemporally dynamic therapy (SDT) is proposed, which converts liquid drug into gel using dynamic chitosan-poly (ethylene glycol) (CP) Schiff-base. It is used to treat oral mucositis (OM) model by dynamically adjusting shape to fit the irregular OM regions and promoting healing. The regenerated tissue has ordered structure.
BIOACTIVE MATERIALS
(2022)
Article
Robotics
Jiazheng Luo, Mingzhi Yuan, Kexue Fu, Manning Wang, Chenxi Zhang
Summary: This letter proposes a deep graph matching based end-to-end learning framework for building dense correspondence between non-rigid point clouds. By formulating correspondence estimation as a graph matching problem and utilizing the topological structure information of graphs, this method outperforms existing methods in both quantitative and qualitative performance, and has better generalization.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Computer Science, Software Engineering
Chao Zhang, Romain Pinquie, Arnaud Polette, Gregorio Carasi, Henri De Charnace, Jean-Philippe Pernot
Summary: This paper presents a two-stage approach for automatically generating 3D CAD models from 2D orthographic drawings. It proposes a pattern-matching algorithm to reconstruct a network of 3D edges by matching 2D edge features extracted from multiple views of the 2D drawing. Besides, it introduces a loop detection algorithm and a clustering algorithm to identify possible faces and reconstruct a watertight 3D CAD model. The approach achieves a high accuracy of 99.59% in reconstructing well-defined models using a public dataset.
COMPUTERS & GRAPHICS-UK
(2023)
Article
Computer Science, Artificial Intelligence
Jiaqi Yang, Ke Xian, Peng Wang, Yanning Zhang
Summary: This paper provides a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods under various application contexts and perturbations, aiming to find a more efficient and accurate way for point-to-point correspondences between 3D rigid data.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Software Engineering
Kangkan Wang, Guofeng Zhang, Jian Yang, Hujun Bao
Summary: This study presents a novel approach for dynamic human body reconstruction and motion tracking using low-cost depth cameras. By combining skeleton-driven deformation and mesh deformation techniques, the system can accurately align the template model with noisy input data. Additionally, a novel data-driven 3D human body model is introduced to efficiently reconstruct human body models with wide shape and pose variations.
Article
Automation & Control Systems
Dazhen Wang, Junxue Ren, Weijun Tian
Summary: This paper studies the influences of modal shape and tool orientation on the dynamic responses during 5-axis ball-end milling of thin-walled parts. A model based on modal shape is established to calculate the dynamic response of the workpiece. The test results show that the model can predict the trend of dynamic response and stability of milling processes.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Geochemistry & Geophysics
Damian Krawczykowski, Aldona Krawczykowska, Tomasz Gawenda
Summary: The properties of bulk materials are influenced by the geometrical features of grains, including size, size distribution, and shape. This study focused on the influence of particle shape on size, using different measuring methods and estimators. Granulometric analyses were conducted on mineral raw material samples with regular and irregular grains. The results of the study provide insights into particle (ir)regularity and aid in selecting appropriate methods for particle assessment.
Article
Mathematics, Applied
Yusha Li, Wenyu Chen, Yiyu Cai, Ahmad Nasri, Jianmin Zheng
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2015)
Article
Computer Science, Software Engineering
Zahraa Yasseen, Anne Verroust-Blondet, Ahmad Nasri
Article
Computer Science, Software Engineering
Erwin de Groot, Brian Wyvill, Loic Barthe, Ahmad Nasri, Paul Lalonde
COMPUTER GRAPHICS FORUM
(2014)
Article
Computer Science, Software Engineering
Z. Yasseen, A. Nasri, W. Boukaram, P. Volino, N. Magnenat-Thalmann
COMPUTER-AIDED DESIGN
(2013)
Article
Computer Science, Software Engineering
Shoichi Okaniwa, Ahmad Nasri, Hongwei Lin, Abdulwahed Abbas, Yuki Kineri, Takashi Maekawa
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2012)
Article
Computer Science, Artificial Intelligence
Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi
Summary: This paper focuses on vehicle trajectory prediction by modeling vehicle interactions, utilizing an attention mechanism to highlight neighboring vehicles' future states, and considering multiple potential futures based on different goals and driving behaviors, leading to outperforming the state-of-the-art performances on highway datasets through a combination of global and partial attention.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
Proceedings Paper
Transportation Science & Technology
Luis Roldao, Raoul de Charette, Anne Verroust-Blondet
2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC)
(2019)
Proceedings Paper
Automation & Control Systems
Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, Fawzi Nashashibi
2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19)
(2019)
Proceedings Paper
Automation & Control Systems
Pierre de Beaucorps, Anne Verroust-Blondet, Renaud Poncelet, Fawzi Nashashibi
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV)
(2018)
Proceedings Paper
Automation & Control Systems
Zayed Alsayed, Guillaume Bresson, Anne Verroust-Blondet, Fawzi Nashashibi
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
(2018)
Article
Computer Science, Information Systems
Esma Elghoul, Anne Verroust-Blondet, Mohamed Chaouch
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Sofiene Mouine, Itheri Yahiaoui, Anne Verroust-Blondet
IMAGE ANALYSIS AND RECOGNITION
(2013)
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)