- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Fast 3D image reconstruction by cuboids and 3D Charlier’s moments
摘要： In this article, we propose a novel approach to accelerate the processing of 3D images by the discrete orthogonal moments of Charlier. The proposed approach is based on two fundamental notions: The first is the acceleration of the computing time of Charlier discrete orthogonal polynomials and moments in the case of the 3D image using digital filters. The second is the description of the 3D image by a set of cuboids of fixed size instead of individual voxels by decomposing the image by cuboids of small sizes to ensure numerical stability. By applying this method, the 3D Charlier moments are calculated from the cuboids instead of the whole image, as the image processing will be locally in each cuboid. This method allows us to speed up the computation time of the moments and to avoid the problem of propagation of digital errors encountered as well when using of digital filters for 3D images of large sizes. The simulation results show the effectiveness of the proposed method in terms of the computation time of the 3D moments of Charlier and in terms of quality of 3D image.
关键词： 3D Charlier moments,Digital filters,3D image reconstruction,3D image cuboid representation
更新于2025-09-23 15:23:52
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Dictionaries of deep features for land-use scene classification of very high spatial resolution images
摘要： Land-use classification in very high spatial resolution images is critical in the remote sensing field. Consequently, remarkable efforts have been conducted towards developing increasingly accurate approaches for this task. In recent years, deep learning has emerged as a dominant paradigm for machine learning, and methodologies based on deep convolutional neural networks have received particular attention from the remote sensing community. These methods typically utilize transfer learning and/or data augmentation to accommodate a small number of labeled images in the publicly available datasets in this field. However, they typically require powerful computers and/or a long time for training. In this work, we propose a simple and novel method for land-use classification in very high spatial resolution images, which efficiently combines transfer learning with a sparse representation. Specifically, the proposed method performs the classification of land-use scenes using a modified version of the well-known sparse representation-based classification method. While this method directly uses the training images to form dictionaries, which are employed to classify test images, our method utilizes a pre-trained deep convolutional neural network and the Gaussian mixture model to generate more robust and compact 'dictionaries of deep features.' The effectiveness of the proposed method was evaluated on two publicly available datasets: UC Merced and Brazilian Cerrado–Savana. The experimental results suggest that our method can potentially outperform state-of-the-art techniques for land-use classification in very high spatial resolution images.
关键词： Dictionary learning,Land-use classification,Sparse representation,Feature learning,Deep learning
更新于2025-09-23 15:23:52
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Radial multipliers in solutions of the Helmholtz equations
摘要： We define radial multipliers using solutions of the Helmholtz equation, which depend on the radial coordinate, and we find the recurrence relations between them in the space of any dimension m > 1, in which the Helmholtz operator is defined. It is shown that the procedure of differentiation of these multipliers leads to a system of solutions of the Helmholtz equation, represented as products of the radial multipliers and harmonic polynomials. Theorems about the properties of radial multipliers and the structure of harmonic polynomials in the solutions of Helmholtz equation are given. These solutions constructed using radial multipliers and harmonic polynomials are proposed to be used in gradient elasticity for multi-layered domains with spherical and cylindrical boundaries, since they allow to present boundary conditions in explicit algebraic form.
关键词： Gradient elasticity,generalized Papkovich–Neuber representation,Helmholtz equation,radial multipliers,harmonic polynomials
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sea Ice Change Detection in SAR Images Based on Collaborative Representation
摘要： Sea ice change detection from synthetic aperture radar (SAR) images is important for navigation safety and natural resource extraction. This paper proposed a sea ice change detection method from SAR images based on collaborative representation. First, neighborhood-based ratio is used to generate a difference image (DI). Then, some reliable samples are selected from the DI by hierarchical fuzzy C-means (FCM) clustering. Finally, based upon these samples, collaborative representation method is utilized to classify pixels from the original SAR images into unchanged and changed class. From there, the final change map can be obtained. Experimental results on two real sea ice datasets demonstrate the superiority of the proposed method over two closely related methods.
关键词： sea ice change detection,synthetic aperture radar,clustering method,collaborative representation
更新于2025-09-23 15:23:52
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SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images
摘要： Context representations have been widely used to profit semantic image segmentation. The emergence of depth data provides additional information to construct more discriminating context representations. Depth data preserves the geometric relationship of objects in a scene, which is generally hard to be inferred from RGB images. While deep convolutional neural networks (CNNs) have been successful in solving semantic segmentation, we encounter the problem of optimizing CNN training for the informative context using depth data to enhance the segmentation accuracy. In this paper, we present a novel switchable context network (SCN) to facilitate semantic segmentation of RGB-D images. Depth data is used to identify objects existing in multiple image regions. The network analyzes the information in the image regions to identify different characteristics, which are then used selectively through switching network branches. With the content extracted from the inherent image structure, we are able to generate effective context representations that are aware of both image structures and object relationships, leading to a more coherent learning of semantic segmentation network. We demonstrate that our SCN outperforms state-of-the-art methods on two public datasets.
关键词： Context representation,convolutional neural network (CNN),RGB-D images,semantic segmentation
更新于2025-09-23 15:23:52
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Blind Quality Index for Tone-Mapped Images Based on Luminance Partition
摘要： Tone-mapping operators (TMOs), which are designed to convert high dynamic range (HDR) images to standard low dynamic range (LDR) images for displaying on conventional devices, have gained extensive attention recently. The quality of tone-mapped images generated by different TMOs varies significantly, which depends upon the image contents and the parameter settings. A quality index that can accurately evaluate the performances of TMOs is thus highly needed. With this motivation, this paper presents a blind quality index based on luminance partition for tone-mapped images. It is based on the fact that the Human Visual System (HVS) has different sensitivities to image regions with different luminance levels. Specifically, two adaptive thresholds are first employed to segment an image into the dark, bright and normal areas. Then, we calculate the quality-aware features from different luminance areas: 1) local entropy feature is extracted from the dark and bright areas to measure the information loss due to the overexposure or underexposure during the tone mapping process; 2) local colorfulness feature is extracted from the normal area to evaluate the reproduction of colors. With the consideration that the perception of image quality depends on the combined effects of the salient local distortion and global quality degradation, the global contrast feature is also calculated and integrated for better evaluation performance. Moreover, to take advantage of the hierarchical characteristic of the HVS, all features are calculated under a multi-resolution framework. Eventually, the extracted features are mapped into an objective quality score based on the random forest regression. The proposed metric is shown to outperform those state-of-the-art metrics according to extensive experiments conducted on two publicly available databases.
关键词： tone-mapped image,multi-resolution representation,Tone-mapping operators,random forest regression,luminance partition,human visual system
更新于2025-09-23 15:23:52
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Material Decomposition in X-ray Spectral CT Using Multiple Constraints in Image Domain
摘要： X-ray spectral CT appears as a new promising imaging modality for the quantitative measurement of materials in an object, compared to conventional energy-integrating CT or dual energy CT. We consider material decomposition in spectral CT as an overcomplete ill-conditioned inverse problem. To solve the problem, we make full use of multi-dimensional nature and high correlation of multi-energy data and spatially neighboring pixels in spectral CT. Meanwhile, we also exploit the fact that material mass density has limited value. The material decomposition is then achieved by using bounded mass density, local joint sparsity and structural low-rank (DSR) in image domain. The results on numerical phantom demonstrate that the proposed DSR method leads to more accurate decomposition than usual pseudo-inverse method with singular value decomposition (SVD) and current popular sparse regularization method with (cid:2)1-norm constraint.
关键词： Sparse representation,X-ray spectral CT,Material decomposition,Low-rank representation
更新于2025-09-23 15:22:29
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An Attribute-based High-level Image Representation for Scene Classification
摘要： Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the 'semantic gap' between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.
关键词： high-level image representation,Scene classification,attribute representation,convolutional neural network
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Fine Registration Technique for Very High Resolution Remote Sensing Images
摘要： This paper presents a novel registration noise (RN) estimation technique for fine registration of very high resolution (VHR) images. This is accomplished by using a two-step strategy to estimate and mitigate residual local misalignments in standardly registered VHR images. The first step takes advantages of the superpixel segmentation and frequency filtering to generate sparse superpixels as the basic objects for RN estimation. Then local rectification is employed for fine registration of the input image under the aid of RN information. More factors are taken into consideration in order to enhance the RN estimation performance. The proposed approach is designed in a fine registration strategy, which can effectively improve the pre-registration result. The experimental results obtained with real datasets confirm the effectiveness of the proposed method.
关键词： local rectification,superpixel segmentation,Fine registration,VHR image,sparse representation
更新于2025-09-23 15:22:29
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Robust Hyperspectral Image Domain Adaptation With Noisy Labels
摘要： In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However, these methods rely on accurate labels in source domain, and seldom consider the performance drop resulted by noisy label, which often happens since labeling pixel in HSI is a challenging task. To improve the robustness of DA method to label noise, we propose a new unsupervised HSI DA method, which is constructed from both feature-level and classifier-level. First, a linear transformation function is learned in feature-level to align the source (domain) subspace with the target (domain) subspace. Then, a robust low-rank representation based classifier is developed to well cope with the features obtained from the aligned subspace. Since both subspace alignment and the classifier are immune to noisy labels, the proposed method obtains good classification results when confronting with noisy labels in source domain. Experimental results on two DA benchmarks demonstrate the effectiveness of the proposed method.
关键词： low-rank representation,hyperspectral image (HSI) classification,Domain adaptation,subspace alignment
更新于2025-09-23 15:22:29