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- 摘要
- 关键词
- 实验方案
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Non-destructive defect evaluation of polymer composites via thermographic data analysis: A manifold learning method
摘要： Recently, various thermographic data analysis methods have been utilized in the field of non-destructive evaluation (NDE) to process thermal images and enhance the visibility of defects. However, most of them extract only linear features, leading to cumbersome results. In this work, manifold learning is introduced into the thermographic data analysis field. As a nonlinear dimensionality reduction technique, manifold learning can identify an intrinsically low-dimensional manifold in a high-dimensional data space. Specifically, an isometric feature mapping (ISOMAP) based manifold learning thermography (MLT) method is proposed to analyze the thermographic data, which can effectively distinguish the uneven background, noise, and defect characteristics contained in thermal images and make the defect detection easier. The feasibility of MLT is illustrated using a carbon fiber-reinforced polymer (CFRP) specimen. The results show that, comparing to the conventional linear methods, the present method can better determine the defect information, including the positions, sizes, and shapes.
关键词： Thermographic data analysis,Non-destructive evaluation,Manifold learning,Active infrared thermography,Carbon fiber-reinforced polymer
更新于2025-09-23 15:22:29
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Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach
摘要： Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.
关键词： partially overlapped images,multi-modality,manifold learning,medical image registration
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Active Manifold Learning for Hyperspectral Image Classification
摘要： Hyperspectral image classification via supervised approaches is often affected by the high dimensionality of the spectral signatures and the relative scarcity of training samples. Dimensionality reduction (DR) and active learning (AL) are two techniques that have been investigated independently to address these two problems. Considering the nonlinear property of the hyperspectral data and the necessity of applying AL adaptively, in this paper, we propose to integrate manifold and active learning into a unique framework to alleviate the aforementioned two issues simultaneously. In particular, supervised Isomap is adopted for DR for the training set, followed by an out-of-sample extension approach to project the large amount of unlabeled samples into previously learned embedding space. Finally, AL is performed in conjunction with k-nearest neighbor (kNN) classification in the embedded feature space. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework in terms of DR and the feature space refinement.
关键词： classification,manifold learning,hyperspectral images,Active learning,out-of-sample extension
更新于2025-09-10 09:29:36
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Target recognition in SAR image based on robust locality discriminant projection
摘要： Extracting valuable and discriminative features is one of the crucial issues for target recognition in synthetic aperture radar (SAR) images. In this study, a feature extraction method based on robust locality discriminant projection (RLDP) is presented for SAR target recognition. To characterise the local structural information of SAR images, the manifold learning technique called the supervised locality preserving projection is introduced to learn a linear projection, with which the SAR image can be cast into an implicit feature space. Then, the authors extend t-distributed stochastic neighbour embedding to a parametric framework for optimising the linear projection. In the resulting feature space, the intrinsic neighbour relation with a certain class can be preserved. In addition, the separation between different classes can be enhanced. Unlike most local manifold learning methods, the proposed method is robust to changes of the neighbour parameter. To further analyse the non-linear structure, a useful variant of RLDP named kernel RLDP (KRLDP) is proposed. KRLDP exploits RLDP in an implicit reproducing kernel Hilbert space, where the kernel-based non-linear projection is learned to capture the non-linear structural information. Extensive experiments on moving and stationary target automatic recognition databases demonstrate the effectiveness of the proposed methods.
关键词： SAR,manifold learning,target recognition,feature extraction,KRLDP,RLDP
更新于2025-09-09 09:28:46