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Kernel Embedding Transformation Learning for Graph Matching. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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2
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Wang R, Yan J, Yang X. Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem With Extension to Hypergraph and Multiple-Graph Matching. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:5261-5279. [PMID: 33961550 DOI: 10.1109/tpami.2021.3078053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's quadratic assignment problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task. The association graph is learned by an embedding network for vertex classification, followed by Sinkhorn normalization and a cross-entropy loss for end-to-end learning. We further improve the embedding model on association graph by introducing Sinkhorn based matching-aware constraint, as well as dummy nodes to deal with unequal sizes of graphs. To our best knowledge, this is one of the first network to directly learn with the general Lawler's QAP. In contrast, recent deep matching methods focus on the learning of node/edge features in two graphs respectively. We also show how to extend our network to hypergraph matching, and matching of multiple graphs. Experimental results on both synthetic graphs and real-world images show its effectiveness. For pure QAP tasks on synthetic data and QAPLIB benchmark, our method can perform competitively and even surpass state-of-the-art graph matching and QAP solvers with notable less time cost. We provide a project homepage at http://thinklab.sjtu.edu.cn/project/NGM/index.html.
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3
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Feature Matching via Motion-Consistency Driven Probabilistic Graphical Model. Int J Comput Vis 2022. [DOI: 10.1007/s11263-022-01644-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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4
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Hu B, Liu Y, Chu P, Tong M, Kong Q. Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection. Front Physiol 2022; 13:911297. [PMID: 35784879 PMCID: PMC9249342 DOI: 10.3389/fphys.2022.911297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 05/24/2022] [Indexed: 11/16/2022] Open
Abstract
Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the objects in medical images are very small but influential. Improving the detection performance of small objects is a very meaningful topic for medical detection. Current researches mainly focus on the extraction of small object features and data augmentation for small object samples, all of these researches focus on extracting the feature space of small objects better. However, in the training process of a detection model, objects of different sizes are mixed together, which may interfere with each other and affect the performance of small object detection. In this paper, we propose a method called pixel level balancing (PLB), which takes into account the number of pixels contained in the detection box as an impact factor to characterize the size of the inspected objects, and uses this as an impact factor. The training loss of each object of different size is adjusted by a weight dynamically, so as to improve the accuracy of small object detection. Finally, through experiments, we demonstrate that the size of objects in object detection interfere with each other. So that we can improve the accuracy of small object detection through PLB operation. This method can perform well with blood cell detection in our experiments.
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Affiliation(s)
- Bin Hu
- Department of Compute Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Liu
- Department of Dermatology, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Laser and Aesthetic Medicine, Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Yang Liu, ; Minglei Tong,
| | - Pengzhi Chu
- Department of Compute Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Minglei Tong
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- *Correspondence: Yang Liu, ; Minglei Tong,
| | - Qingjie Kong
- Riseye Research, Riseye Intelligent Technology (Shanghai) Co., Ltd., Shanghai, China
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6
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Li J, Gao C, Yin P. Non-Interlaced Dynamic Time Warping for Distance Between Matrixes. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10739-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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7
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Graph matching based point correspondence with alternating direction method of multipliers. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Zhu H, Cui C, Deng L, Cheung RCC, Yan H. Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:4062-4074. [PMID: 31536028 DOI: 10.1109/tcyb.2019.2936176] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The procedure of establishing the correspondence between two sets of feature points is important in computer vision applications. In this article, an elastic net constraint-based tensor model is proposed for high-order graph matching. To control the tradeoff between the sparsity and the accuracy of the matching results, an elastic net constraint is introduced into the tensor-based graph matching model. Then, a nonmonotone spectral projected gradient (NSPG) method is derived to solve the proposed matching model. During the optimization of using NSPG, we propose an algorithm to calculate the projection on the feasible convex sets of elastic net constraint. Further, the global convergence of solving the proposed model using the NSPG method was proved. The superiority of the proposed method is verified through experiments on the synthetic data and natural images.
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Sahloul H, Shirafuji S, Ota J. An Accurate and Efficient Voting Scheme for a Maximally All-Inlier 3D Correspondence Set. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021; 43:2287-2298. [PMID: 31940518 DOI: 10.1109/tpami.2020.2963980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We present a highly accurate and efficient, yet simple, two-stage voting scheme for distinguishing inlier 3D correspondences by densely assessing and ranking their local and global geometric consistencies. The strength of the proposed method stems from both the novel idea of post-validated voting set, as well as single-point superimposition transforms, which are computationally cheap and avoid rotational ambiguities. Using a well-known dataset consisting of various 3D models and numerous scenes that include different occlusion rates, the proposed scheme is evaluated against state-of-the-art 3D voting schemes, in terms of both the correspondence PR (precision-recall) AUC (area under curve), and the execution time. A total of 374 experiments were conducted for each method, which involved a combination of four models, 50 scenes, and two down-samplings. The proposed scheme outperforms the state-of-the-art 3D voting schemes in terms of both accuracy and speed. Quantitatively, the proposed scheme scores 97.0% ±12.9% on the PR AUC metric, averaged over all of the experiments, while the two state-of-the-art schemes score 74.2% ±22.2% and 78.3% ±26.4%. Furthermore, the proposed scheme requires only 24.1% ±6.0% of the time consumed by the fastest state-of-the-art scheme. The proposed voting scheme also demonstrates high robustness against occlusions and scarce inliers.
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Abelé R, Damoiseaux JL, Moubtahij RE, Boi JM, Fronte D, Liardet PY, Merad D. Spatial Location in Integrated Circuits through Infrared Microscopy. SENSORS 2021; 21:s21062175. [PMID: 33804619 PMCID: PMC8003807 DOI: 10.3390/s21062175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/11/2021] [Accepted: 03/17/2021] [Indexed: 11/16/2022]
Abstract
In this paper, we present an infrared microscopy based approach for structures' location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented-discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.
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Affiliation(s)
- Raphaël Abelé
- Laboratoire d’Informatique et Systemes, Aix-Marseille University, 163 Avenue de Luminy, 13288 CEDEX 09 Marseille, France; (J.-L.D.); (R.E.M.); (J.-M.B.)
- Correspondence: (R.A.); (D.M.)
| | - Jean-Luc Damoiseaux
- Laboratoire d’Informatique et Systemes, Aix-Marseille University, 163 Avenue de Luminy, 13288 CEDEX 09 Marseille, France; (J.-L.D.); (R.E.M.); (J.-M.B.)
| | - Redouane El Moubtahij
- Laboratoire d’Informatique et Systemes, Aix-Marseille University, 163 Avenue de Luminy, 13288 CEDEX 09 Marseille, France; (J.-L.D.); (R.E.M.); (J.-M.B.)
| | - Jean-Marc Boi
- Laboratoire d’Informatique et Systemes, Aix-Marseille University, 163 Avenue de Luminy, 13288 CEDEX 09 Marseille, France; (J.-L.D.); (R.E.M.); (J.-M.B.)
| | - Daniele Fronte
- STMicroelectronics, 190 Avenue Coq, 13106 Rousset, France;
| | | | - Djamal Merad
- Laboratoire d’Informatique et Systemes, Aix-Marseille University, 163 Avenue de Luminy, 13288 CEDEX 09 Marseille, France; (J.-L.D.); (R.E.M.); (J.-M.B.)
- Correspondence: (R.A.); (D.M.)
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Lin W, Li Y, Xiao H, See J, Zou J, Xiong H, Wang J, Mei T. Group Reidentification with Multigrained Matching and Integration. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:1478-1492. [PMID: 31199281 DOI: 10.1109/tcyb.2019.2917713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The task of reidentifying groups of people under different camera views is an important yet less-studied problem. Group reidentification (Re-ID) is a very challenging task since it is not only adversely affected by common issues in traditional single-object Re-ID problems, such as viewpoint and human pose variations, but also suffers from changes in group layout and group membership. In this paper, we propose a novel concept of group granularity by characterizing a group image by multigrained objects: individual people and subgroups of two and three people within a group. To achieve robust group Re-ID, we first introduce multigrained representations which can be extracted via the development of two separate schemes, that is, one with handcrafted descriptors and another with deep neural networks. The proposed representation seeks to characterize both appearance and spatial relations of multigrained objects, and is further equipped with importance weights which capture variations in intragroup dynamics. Optimal group-wise matching is facilitated by a multiorder matching process which, in turn, dynamically updates the importance weights in iterative fashion. We evaluated three multicamera group datasets containing complex scenarios and large dynamics, with experimental results demonstrating the effectiveness of our approach.
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Yu YF, Xu G, Jiang M, Zhu H, Dai DQ, Yan H. Joint Transformation Learning via the L 2,1-Norm Metric for Robust Graph Matching. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:521-533. [PMID: 31059466 DOI: 10.1109/tcyb.2019.2912718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness on the matching graphs with deformations, rotations, outliers, and noise. First, we embed the joint geometric transformation into the graph matching model, which performs unary matching over graph nodes and local structure matching over graph edges simultaneously. Then, the L2,1 -norm is used as the similarity metric in the presented RGM to enhance the robustness. Finally, we derive an objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed algorithm. Extensive experiments on various graph matching tasks, such as outliers, rotations, and deformations show that the proposed RGM model achieves competitive performance compared to the existing methods.
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Xia CQ, Pan X, Yang Y, Huang Y, Shen HB. Recent Progresses of Computational Analysis of RNA-Protein Interactions. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11315-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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14
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Mirakhorli J, Amindavar H, Mirakhorli M. A new method to predict anomaly in brain network based on graph deep learning. Rev Neurosci 2020; 31:681-689. [PMID: 32678803 DOI: 10.1515/revneuro-2019-0108] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 02/01/2020] [Indexed: 12/15/2022]
Abstract
Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer's disease.
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Affiliation(s)
- Jalal Mirakhorli
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Hamidreza Amindavar
- Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Mojgan Mirakhorli
- Medical Genetic Laboratory, Iranian Comprehensive Hemophilia Care Center (ICHCC), Tehran, Iran
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15
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Yao S, Yan J, Wu M, Yang X, Zhang W, Lu H, Qian B. Texture Synthesis Based Thyroid Nodule Detection From Medical Ultrasound Images: Interpreting and Suppressing the Adversarial Effect of In-place Manual Annotation. Front Bioeng Biotechnol 2020; 8:599. [PMID: 32626697 PMCID: PMC7311795 DOI: 10.3389/fbioe.2020.00599] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 05/15/2020] [Indexed: 11/26/2022] Open
Abstract
Deep learning method have been offering promising solutions for medical image processing, but failing to understand what features in the input image are captured and whether certain artifacts are mistakenly included in the model, thus create crucial problems in generalizability of the model. We targeted a common issue of this kind caused by manual annotations appeared in medical image. These annotations are usually made by the doctors at the spot of medical interest and have adversarial effect on many computer vision AI tasks. We developed an inpainting algorithm to remove the annotations and recover the original images. Besides we applied variational information bottleneck method in order to filter out the unwanted features and enhance the robustness of the model. Our impaiting algorithm is extensively tested in object detection in thyroid ultrasound image data. The mAP (mean average precision, with IoU = 0.3) is 27% without the annotation removal. The mAP is 83% if manually removed the annotations using Photoshop and is enhanced to 90% using our inpainting algorithm. Our work can be utilized in future development and evaluation of artificial intelligence models based on medical images with defects.
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Affiliation(s)
- Siqiong Yao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Junchi Yan
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Mingyu Wu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xue Yang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Weituo Zhang
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Lu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Biyun Qian
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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16
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Zhu X, Li Z, Li X, Li S, Dai F. Attention-aware perceptual enhancement nets for low-resolution image classification. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.12.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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Shen Y, Ke J. A Deformable CRF Model for Histopathology Whole-Slide Image Classification. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2020 2020. [DOI: 10.1007/978-3-030-59722-1_48] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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18
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Li Y, Li Q, Liu Y, Xie W. A spatial-spectral SIFT for hyperspectral image matching and classification. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2018.08.032] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Gao X, Shen S, Hu Z, Wang Z. Ground and aerial meta-data integration for localization and reconstruction: A review. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2018.07.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Ma J, Jiang X, Jiang J, Guo X. Robust Feature Matching Using Spatial Clustering with Heavy Outliers. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 29:736-746. [PMID: 31449018 DOI: 10.1109/tip.2019.2934572] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This paper focuses on removing mismatches from given putative feature matches created typically based on descriptor similarity. To achieve this goal, existing attempts usually involve estimating the image transformation under a geometrical constraint, where a pre-defined transformation model is demanded. This severely limits the applicability, as the transformation could vary with different data and is complex and hard to model in many real-world tasks. From a novel perspective, this paper casts the feature matching into a spatial clustering problem with outliers. The main idea is to adaptively cluster the putative matches into several motion consistent clusters together with an outlier/mismatch cluster. To implement the spatial clustering, we customize the classic density based spatial clustering method of applications with noise (DBSCAN) in the context of feature matching, which enables our approach to achieve quasi-linear time complexity. We also design an iterative clustering strategy to promote the matching performance in case of severely degraded data. Extensive experiments on several datasets involving different types of image transformations demonstrate the superiority of our approach over state-of-the-art alternatives. Our approach is also applied to near-duplicate image retrieval and co-segmentation and achieves promising performance.
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21
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Zhang XY, Shi H, Zhu X, Li P. Active semi-supervised learning based on self-expressive correlation with generative adversarial networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.083] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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22
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Spectral-Spatial Attention Networks for Hyperspectral Image Classification. REMOTE SENSING 2019. [DOI: 10.3390/rs11080963] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.
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Chen J, Xu Q, Luo L, Wang Y, Wang S. A Robust Method for Automatic Panoramic UAV Image Mosaic. SENSORS 2019; 19:s19081898. [PMID: 31013567 PMCID: PMC6515254 DOI: 10.3390/s19081898] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/02/2019] [Accepted: 04/16/2019] [Indexed: 11/17/2022]
Abstract
This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect.
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Affiliation(s)
- Jun Chen
- School of Automation, China University of Geosciences, Wuhan 430074, China.
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
- Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430073, China.
| | - Quan Xu
- School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China.
| | - Linbo Luo
- School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China.
| | - Yongtao Wang
- School of Automation, China University of Geosciences, Wuhan 430074, China.
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
| | - Shuchun Wang
- School of Automation, China University of Geosciences, Wuhan 430074, China.
- Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
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Du Q, Xu H, Ma Y, Huang J, Fan F. Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model. SENSORS 2018; 18:s18113827. [PMID: 30413066 PMCID: PMC6263655 DOI: 10.3390/s18113827] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 11/03/2018] [Accepted: 11/05/2018] [Indexed: 11/23/2022]
Abstract
In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.
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Affiliation(s)
- Qinglei Du
- Electronic Information School, Wuhan University, Wuhan 430072, China.
- Air Force Early Warning Academy, Wuhan 430019, China.
| | - Han Xu
- Electronic Information School, Wuhan University, Wuhan 430072, China.
| | - Yong Ma
- Electronic Information School, Wuhan University, Wuhan 430072, China.
| | - Jun Huang
- Electronic Information School, Wuhan University, Wuhan 430072, China.
| | - Fan Fan
- Electronic Information School, Wuhan University, Wuhan 430072, China.
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Abstract
Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
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