Article
Computer Science, Theory & Methods
Zhuohao Jia, Simon Liao
Summary: This article presents a novel GPU-based method for efficiently computing high-order Zernike moments and demonstrates its effectiveness in computing 500-order Zernike moments within 0.5 seconds for a 512x512 image.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Xiaoqi Lu, Jianwei Yang
Summary: Zernike moments (ZMs) are widely used orthogonal moments, but low-order ZMs have limitations in describing small size images. Fractional Zernike moments (FrZMs) can handle small size images, but high-order FrZMs suffer from numerical instability. To overcome these issues, transformed Zernike moments (TZMs) and logarithmic Zernike moments (LoZMs) are introduced.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Zengjun Zhao, Xinkai Kuang, Yukuan Zhu, Yecheng Liang, Yubo Xuan
Summary: Zernike moments, a representative orthogonal moment, have been widely used in image processing and pattern recognition. A novel algorithm is proposed in this paper to increase resource utilization and reduce calculation amount by combining kernels and optimizing radial polynomials. Experimental results show improved computational performance and precision with no compromise compared to typical accurate algorithms.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Computer Science, Information Systems
Manoj K. Singh, Sanoj Kumar, Gaurav Bhatnagar, Deepika Saini, Musrrat Ali, Chandra Mani Sharma, Navel Sharma
Summary: Accurate computation of image moments is crucial for various applications, such as image reconstruction and object recognition. This study proposes a method that uses the entire unit circle as an integration domain to improve the precision and rotational/scaling invariance of image moments. Experimental results show that this technique outperforms other state-of-the-art methods in terms of accuracy and invariance.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
(2022)
Article
Engineering, Marine
Wenhan Gao, Shanmin Zhou, Shuo Liu, Tao Wang, Bingbing Zhang, Tian Xia, Yong Cai, Jianxing Leng
Summary: Sonar images have lower resolution and blurrier edges compared to optical images, which leads to less robust feature-matching method in underwater target tracking. To solve this problem, a particle filter-based underwater target-tracking method utilizing Zernike moment feature matching is proposed. Zernike moments are used to construct the feature-description vector and contribute to the update of particle weights.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Review
Engineering, Electrical & Electronic
Chandan Singh, Jaspreet Singh
Summary: The paper reviews rotation invariant techniques based on moments and transforms, and conducts comparative performance analysis. The results show that the magnitude of moments of the gray-scale and multi-channel representation of color objects provides the best recognition results.
Article
Engineering, Aerospace
Yutian Liang, Weimin Song, Haixin Chen, Yufei Zhang
Summary: The study revealed significant impact of casing treatment on stall mechanism, especially when the rotor tip becomes unloaded, leading to decreased compressor stability. Therefore, potential harmful effects of casing treatment on stability margin should be considered when applied in different operating conditions.
JOURNAL OF AEROSPACE ENGINEERING
(2021)
Review
Optics
Kuo Niu, Chao Tian
Summary: Zernike polynomials, a set of continuous orthogonal functions over a unit circle, have found widespread applications in various fields. This review comprehensively presents the history, definitions, mathematical properties, applications, and connections with other polynomials of Zernike polynomials, aiming to clarify confusion, provide a reference for beginners and specialists, and promote further developments and applications.
Article
Computer Science, Artificial Intelligence
J. Saul Rivera-Lopez, Cesar Camacho-Bello, Horlando Vargas-Vargas, Alicia Escamilla-Noriega
Summary: This article proposes a new method for fast and efficient calculation of 3D Tchebichef moments, integrating the Kronecker tensor product for higher orders with the advantage of parallelization. Experimental results clearly demonstrate the benefits and effectiveness of this method compared to existing ones.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2022)
Article
Mathematics, Applied
Yinkun Wang
Summary: In this work, a new class of parallel time integrators based on Picard iteration is proposed for initial-value problems. Sequential integrators called numerical Picard iteration methods are investigated, which belong to the framework of deferred correction methods. It is shown that numerical Picard iteration methods have a min(J, M + 1)-order rate of convergence. A class of parallel solvers is then proposed to simultaneously and nearly constantly proceed J Picard iterations. The parallel solvers yield the same convergence rate as the numerical Picard iteration methods.
JOURNAL OF SCIENTIFIC COMPUTING
(2023)
Article
Computer Science, Information Systems
Ilham El Ouariachi, Rachid Benouini, Khalid Zenkouar, Arsalane Zarghili, Hakim El Fadili
Summary: This paper proposes a new set of RGB-D feature extraction method FA-MCJMI based on Image Moment Invariants, which enhances the numerical accuracy and improves the computational speed of MCJMIs. The superiority of the new FA-MCJMI set is demonstrated in hand gesture representation and recognition.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Thomas Theodoridis, Kostas Loumponias, Nicholas Vretos, Petros Daras
Summary: Most neural network architectures in computer vision consist of the same building blocks, but a generalization of the traditional average pooling operator using Zernike moments has shown superior performance on experimental datasets compared to baseline approaches. Significant gains in classification accuracy can be achieved with only a modest increase in training time.
Article
Chemistry, Analytical
Hima Deepthi Vankayalapati, Swarna Kuchibhotla, Mohan Sai Kumar Chadalavada, Shashi Kant Dargar, Koteswara Rao Anne, Kyandoghere Kyamakya
Summary: This study proposes a real-time head pose and gaze estimation algorithm that combines appearance and geometric methods for feature extraction and uses conventional discriminant algorithms for classification. The experiments demonstrate that the algorithm achieves high accuracy and stability under different illumination conditions.
Article
Materials Science, Multidisciplinary
Haixin Luo, Jie Xu, Jiannan Jiao, Liyun Zhong, Xiaoxu Lu, Jindong Tian
Summary: Compact computational in-line holography based on deep learning is a promising single-shot method for imaging microparticles dispersed in 3D volume. The shape of the microparticles contains valuable information for species classification, but obtaining a dataset for network training is time-consuming and inefficient due to the complex and varied 2D shapes of different particle species. In this study, a moment-based shape-learning holography (MSLH) is proposed, which mathematically characterizes the shape of a microparticle using varying weights of Zernike moments. By decomposing and recombining feature shapes of pollen particles into new characteristic shapes, MSLH improves the efficiency of dataset preparation. The depth-encoded shape-learning is achieved using a U-net with self-attention mechanism, enabling fast axial depth determination. The shape reconstruction uses a wavelet-based method with more explicit physical meanings, making MSLH a hybrid data-and-model driven approach that requires fewer primary data. Validation results demonstrate that MSLH achieves high accuracy in axial position and shape reconstruction, while maintaining good classification effectiveness. MSLH is believed to be an easy-to-setup, efficient-to-construct, and fast-to-output approach for shape-based classifications of 3D distributed microparticles in dynamic fluid.
ADVANCED PHOTONICS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Runwen Hu, Shijun Xiang
Summary: This article presents a new cover-lossless robust image watermarking method by embedding a watermark into low-order Zernike moments, achieving good performance against geometric deformations.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
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)