This page has only limited features, please log in for full access.

Unclaimed
Tao Lei
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 14 August 2021 in Symmetry
Reads 0
Downloads 0

Overlap function (which has symmetry and continuity) is widely used in image processing, data classification, and multi-attribute decision making problems. In recent years, theoretical research on overlap function has been extended to interval valued overlap function and lattice valued overlap function, but intuitionistic fuzzy overlap function (IF-overlap function) has not been studied. In this paper, the concept of IF-overlap function is proposed for the first time, then the generating method of IF-overlap function is given. The representable IF-overlap function is defined, and the concrete examples of representable and unrepresentable IF-overlap functions are given. Moreover, a new class of intuitionistic fuzzy rough set (IF-roght set) model is proposed by using IF-overlap function and its residual implication, which extends the IF-rough set model based on intuitionistic fuzzy triangular norm, and the basic properties of the new intuitionistic fuzzy upper and lower approximate operators are analyzed and studied. At the same time, the established IF-rough set based on IF-overlap function is applied to MCDM (multi-criteria decision-making) problems, the intuitionistic fuzzy TOPSIS method is improved. Through the comparative analysis of some cases, the new method is proved to be flexible and effective.

ACS Style

Xiaofeng Wen; Xiaohong Zhang; Tao Lei. Intuitionistic Fuzzy (IF) Overlap Functions and IF-Rough Sets with Applications. Symmetry 2021, 13, 1494 .

AMA Style

Xiaofeng Wen, Xiaohong Zhang, Tao Lei. Intuitionistic Fuzzy (IF) Overlap Functions and IF-Rough Sets with Applications. Symmetry. 2021; 13 (8):1494.

Chicago/Turabian Style

Xiaofeng Wen; Xiaohong Zhang; Tao Lei. 2021. "Intuitionistic Fuzzy (IF) Overlap Functions and IF-Rough Sets with Applications." Symmetry 13, no. 8: 1494.

Journal article
Published: 26 July 2021 in IEEE Transactions on Fuzzy Systems
Reads 0
Downloads 0

Fuzzy c-means (FCM) algorithms with spatial information have been widely applied in the field of image segmentation. However, most of them suffer from two challenges. One is that introduction of fixed or adaptive single neighboring information with narrow receptive field limits contextual constraints leading to clutter segmentations. The other is that incorporation of superpixels with wide receptive field enlarges spatial coherency leading to block effects. To address these challenges, we propose fuzzy Students t-distribution model based on richer spatial combination (FRSC) for image segmentation. In this Paper, we make two significant contributions. The first is that both narrow and wide receptive fields are integrated into the objective function of FRSC, which is convenient to mine image features and distinguish local difference. The second is that the rich spatial combination under Students t-distribution ensures that spatial information is introduced into the updated parameters of FRSC,which is helpful in finding a balance between the noise-immunity and detail-preservation. Experimental results on synthetic and publicly available images, further demonstrate that the proposed FRSC addresses successfully the limitations of FCM algorithms with spatial information and provides better segmentation results than state-of-the-art clustering algorithms.

ACS Style

Tao Lei; Xiaohong Jia; Dinghua Xue; Qi Wang; Hongying Meng; Asoke K Nandi. Fuzzy Students T-Distribution Model Based on Richer Spatial Combination. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Tao Lei, Xiaohong Jia, Dinghua Xue, Qi Wang, Hongying Meng, Asoke K Nandi. Fuzzy Students T-Distribution Model Based on Richer Spatial Combination. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Tao Lei; Xiaohong Jia; Dinghua Xue; Qi Wang; Hongying Meng; Asoke K Nandi. 2021. "Fuzzy Students T-Distribution Model Based on Richer Spatial Combination." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Journal article
Published: 01 July 2021 in Sensors
Reads 0
Downloads 0

Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear. Such a “black box” only tells a result but not what CNN learned from the input data, thus it is difficult for researchers to further analyze the causes of errors. Layer-wise relevance propagation (LRP) is a prevalent pixel-level rearrangement algorithm to visualize neural networks’ inner mechanism. LRP is usually applied in sparse auto-encoder with only fully-connected layers rather than CNN, but such network structure usually obtains much lower recognition accuracy than CNN. In this paper, we propose a novel LRP algorithm particularly designed for understanding CNN’s performance on SAR image target recognition. We provide a concise form of the correlation between output of a layer and weights of the next layer in CNNs. The proposed method can provide positive and negative contributions in input SAR images for CNN’s classification, viewed as a clear visual understanding of CNN’s recognition mechanism. Numerous experimental results demonstrate the proposed method outperforms common LRP.

ACS Style

Bo Zang; Linlin Ding; Zhenpeng Feng; Mingzhe Zhu; Tao Lei; Mengdao Xing; Xianda Zhou. CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images. Sensors 2021, 21, 4536 .

AMA Style

Bo Zang, Linlin Ding, Zhenpeng Feng, Mingzhe Zhu, Tao Lei, Mengdao Xing, Xianda Zhou. CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images. Sensors. 2021; 21 (13):4536.

Chicago/Turabian Style

Bo Zang; Linlin Ding; Zhenpeng Feng; Mingzhe Zhu; Tao Lei; Mengdao Xing; Xianda Zhou. 2021. "CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images." Sensors 21, no. 13: 4536.

Conference paper
Published: 27 June 2021 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

For semantic segmentation of high-resolution remote-sensing images, digital surface models (DSMs) information is useful for improving the accuracy and robustness of the segmentation models. However, since the feature distributions of spectral and DSM images vary significantly in different scenes, it is difficult to fuse them effectively in popular deep network models. To solve this issue, we propose an attention-based DSM fusion network (ADF-Net) for high-resolution remote-sensing image semantic segmentation. The proposed network makes two contributions. The first is that we design an attention-based feature fusion module, which can selectively gather features from spectral and DSM information by channel attention mechanism, and further combine them to get high-quality fusion features. The second is that we introduce a residual feature refinement module to reduce the redundant information from skip connection adaptively. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets, experimental results demonstrate that our model outperforms state-of-the-art methods.

ACS Style

Minfei Lu; Yuxiao Zhang; Xiaogang Du; Tao Chen; Shigang Liu; Tao Lei. Attention-Based DSM Fusion Network for Semantic Segmentation of High-Resolution Remote-Sensing Images. Advances in Intelligent Systems and Computing 2021, 610 -618.

AMA Style

Minfei Lu, Yuxiao Zhang, Xiaogang Du, Tao Chen, Shigang Liu, Tao Lei. Attention-Based DSM Fusion Network for Semantic Segmentation of High-Resolution Remote-Sensing Images. Advances in Intelligent Systems and Computing. 2021; ():610-618.

Chicago/Turabian Style

Minfei Lu; Yuxiao Zhang; Xiaogang Du; Tao Chen; Shigang Liu; Tao Lei. 2021. "Attention-Based DSM Fusion Network for Semantic Segmentation of High-Resolution Remote-Sensing Images." Advances in Intelligent Systems and Computing , no. : 610-618.

Original research paper
Published: 09 June 2021 in IET Image Processing
Reads 0
Downloads 0

Although deep learning has been widely used for dense crowd counting, it still faces two challenges. Firstly, the popular network models are sensitive to scale variance of human head, human occlusions, and complex background due to repeated utilization of vanilla convolution kernels. Secondly, the vanilla feature fusion often depends on summation or concatenation, which ignores the correlation of different features leading to information redundancy and low robustness to background noise. To address these issues, a multi-scale feature pyramid network (MFP-Net) for dense crowd counting is proposed in this paper. The proposed MFP-Net makes two contributions. Firstly, the feature pyramid fusion module is designed that adopts rich convolutions with different depths and scales, not only to expand the receptive field, but also to improve the inference speed of models by using parallel group convolution. Secondly, a feature attention-aware module is added in the feature fusion stage. The module can achieve local and global information fusion by capturing the importance of the spatial and channel domains to improve model robustness. The proposed MFP-Net is evaluated on five publicly available datasets, and experiments show that the MFP-Net not only provides better crowd counting results than comparative models, but also requires fewer parameters.

ACS Style

Tao Lei; Dong Zhang; Risheng Wang; Shuying Li; Weijiang Zhang; Asoke K. Nandi. MFP‐Net: Multi‐scale feature pyramid network for crowd counting. IET Image Processing 2021, 1 .

AMA Style

Tao Lei, Dong Zhang, Risheng Wang, Shuying Li, Weijiang Zhang, Asoke K. Nandi. MFP‐Net: Multi‐scale feature pyramid network for crowd counting. IET Image Processing. 2021; ():1.

Chicago/Turabian Style

Tao Lei; Dong Zhang; Risheng Wang; Shuying Li; Weijiang Zhang; Asoke K. Nandi. 2021. "MFP‐Net: Multi‐scale feature pyramid network for crowd counting." IET Image Processing , no. : 1.

Original research paper
Published: 13 May 2021 in IET Image Processing
Reads 0
Downloads 0

Many biomedical applications require accurate non‐rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log‐Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nystr o ̈ m approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log‐Demons. Finally, the hybrid multi‐resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log‐Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state‐of‐the‐art algorithms.

ACS Style

Xiaogang Du; Dongxin Gu; Tao Lei; Song Wang; Xuejun Zhang; Hongying Meng. HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features. IET Image Processing 2021, 15, 2666 -2679.

AMA Style

Xiaogang Du, Dongxin Gu, Tao Lei, Song Wang, Xuejun Zhang, Hongying Meng. HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features. IET Image Processing. 2021; 15 (11):2666-2679.

Chicago/Turabian Style

Xiaogang Du; Dongxin Gu; Tao Lei; Song Wang; Xuejun Zhang; Hongying Meng. 2021. "HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features." IET Image Processing 15, no. 11: 2666-2679.

Journal article
Published: 08 March 2021 in IEEE Transactions on Cognitive and Developmental Systems
Reads 0
Downloads 0

Existing face anti-spoofing models using deep learning for multi-modality data suffer from low generalization in the case of using variety of presentation attacks such as 2D printing and high-precision 3D face masks. One of the main reasons is that the non-linearity of multi-spectral information used to preserve the intrinsic attributes between a real and a fake face are not well extracted. To address this issue, we propose a multi-modility data based two-stage cascade framework for face anti-spoofing. The proposed framework has two advantages. Firstly, we design a two-stage cascade architecture that can selectively fuse low-level and high-level features from different modalities to improve feature representation. Secondly, we use multi-modality data to construct a distance-free spectral on RGB and infrared (IR) to augment the non-linearity of data. The presented data fusion strategy is different from popular fusion approaches, since it can strengthen discrimination ability of network models on physical attribute features than identity structure features under certain constraints. In addition, a multi-scale patch based weighted fine-tuning strategy is designed to learn each specific local face region. Experimental results show that the proposed framework achieves better performance than other state-of-the-art methods on both benchmark datasets and self-established datasets, especially on multi-material masks spoofing.

ACS Style

Weihua Liu; Xiaokang Wei; Tao Lei; Xingwu Wang; Hongying Meng; Asoke K. Nandi. Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing. IEEE Transactions on Cognitive and Developmental Systems 2021, PP, 1 -1.

AMA Style

Weihua Liu, Xiaokang Wei, Tao Lei, Xingwu Wang, Hongying Meng, Asoke K. Nandi. Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing. IEEE Transactions on Cognitive and Developmental Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Weihua Liu; Xiaokang Wei; Tao Lei; Xingwu Wang; Hongying Meng; Asoke K. Nandi. 2021. "Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing." IEEE Transactions on Cognitive and Developmental Systems PP, no. 99: 1-1.

Journal article
Published: 16 February 2021 in IEEE Transactions on Radiation and Plasma Medical Sciences
Reads 0
Downloads 0

Deep convolutional neural networks have been widely used for medical image segmentation due to their superiority in feature learning. Although these networks are successful for simple object segmentation tasks, they suffer from two problems for liver and liver tumor segmentation in CT images. One is that convolutional kernels of fixed geometrical structure are unmatched with livers and liver tumors of irregular shapes. The other is that pooling and strided convolutional operations easily lead to the loss of spatial contextual information of images. To address these issues, we propose a deformable encoder-decoder network (DefED-Net) for liver and liver tumor segmentation. The proposed network makes two contributions. The first is that the deformable convolution is used to enhance the feature representation capability of DefED-Net, which can help the network to learn convolution kernels with adaptive spatial structuring information. The second is that we design a Ladder-atrous-spatial-pyramid-pooling module using multi-scale dilation rate (Ladder-ASPP) and apply the module to learn better context information than the atrous spatial pyramid pooling (ASPP) for CT image segmentation. The proposed DefED-Net is evaluated on two public benchmark datasets, the LiTS and the 3DIRCADb. Experiments demonstrate that the DefED-Net has better capability of feature representation as well as provides higher accuracy on liver and liver tumor segmentation than stateof-the art networks. The available code of DefED-Net we propose can be found from https://github.com/SUST-reynole/DefED-Net.

ACS Style

Tao Lei; Risheng Wang; Yuxiao Zhang; Yong Wan; Chang Liu; Asoke K. Nandi. DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation. IEEE Transactions on Radiation and Plasma Medical Sciences 2021, PP, 1 -1.

AMA Style

Tao Lei, Risheng Wang, Yuxiao Zhang, Yong Wan, Chang Liu, Asoke K. Nandi. DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021; PP (99):1-1.

Chicago/Turabian Style

Tao Lei; Risheng Wang; Yuxiao Zhang; Yong Wan; Chang Liu; Asoke K. Nandi. 2021. "DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation." IEEE Transactions on Radiation and Plasma Medical Sciences PP, no. 99: 1-1.

Journal article
Published: 23 December 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Reads 0
Downloads 0

Popular unsupervised change detection algorithms suffer from two problems: firstly, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change; secondly, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multi-scale and multi-resolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multi-scale and frequency multi-resolution information fusion (SM-FM), which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution (VHR) remote sensing images and synthetic aperture radar (SAR) images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches.

ACS Style

Dinghua Xue; Tao Lei; Xiaohong Jia; Xingwu Wang; Tao Chen; Asoke K. Nandi. Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1796 -1809.

AMA Style

Dinghua Xue, Tao Lei, Xiaohong Jia, Xingwu Wang, Tao Chen, Asoke K. Nandi. Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1796-1809.

Chicago/Turabian Style

Dinghua Xue; Tao Lei; Xiaohong Jia; Xingwu Wang; Tao Chen; Asoke K. Nandi. 2020. "Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1796-1809.

Journal article
Published: 09 December 2020 in IEEE Transactions on Neural Systems and Rehabilitation Engineering
Reads 0
Downloads 0

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it’s difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

ACS Style

Bingtao Zhang; Guanghui Yan; Zhifei Yang; Yun Su; Jinfeng Wang; Tao Lei. Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 29, 215 -229.

AMA Style

Bingtao Zhang, Guanghui Yan, Zhifei Yang, Yun Su, Jinfeng Wang, Tao Lei. Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 29 (99):215-229.

Chicago/Turabian Style

Bingtao Zhang; Guanghui Yan; Zhifei Yang; Yun Su; Jinfeng Wang; Tao Lei. 2020. "Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, no. 99: 215-229.

Journal article
Published: 24 November 2020 in IEEE Access
Reads 0
Downloads 0

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.

ACS Style

Xiaohong Jia; Tao Lei; Peng Liu; Dinghua Xue; Hongying Meng; Asoke K. Nandi. Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering. IEEE Access 2020, 8, 211526 -211539.

AMA Style

Xiaohong Jia, Tao Lei, Peng Liu, Dinghua Xue, Hongying Meng, Asoke K. Nandi. Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering. IEEE Access. 2020; 8 (99):211526-211539.

Chicago/Turabian Style

Xiaohong Jia; Tao Lei; Peng Liu; Dinghua Xue; Hongying Meng; Asoke K. Nandi. 2020. "Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering." IEEE Access 8, no. 99: 211526-211539.

Journal article
Published: 10 August 2020 in IEEE Access
Reads 0
Downloads 0

Traditional fuzzy clustering algorithms suffer from two problems in image segmentations. One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy memberships. The other is that these algorithms often cause image over-segmentation due to the loss of image local spatial information. To address these issues, we propose a robust self-sparse fuzzy clustering algorithm (RSSFCA) for image segmentation. The proposed RSSFCA makes two contributions. The first concerns a regularization under Gaussian metric that is integrated into the objective function of fuzzy clustering algorithms to obtain fuzzy membership with sparsity, which reduces a proportion of noisy features and improves clustering results. The second concerns a connected-component filtering based on area density balance strategy (CCF-ADB) that is proposed to address the problem of image over-segmentation. Compared to the integration of local spatial information into the objective functions, the presented CCF-ADB is simpler and faster for the removal of small areas. Experimental results show that the proposed RSSFCA addresses two problems in current fuzzy clustering algorithms, i.e., the outlier sensitivity and the over-segmentation, and it provides better image segmentation results than state-of-the-art algorithms.

ACS Style

Xiaohong Jia; Tao Lei; Xiaogang Du; Shigang Liu; Hongying Meng; Asoke K. Nandi. Robust Self-Sparse Fuzzy Clustering for Image Segmentation. IEEE Access 2020, 8, 146182 -146195.

AMA Style

Xiaohong Jia, Tao Lei, Xiaogang Du, Shigang Liu, Hongying Meng, Asoke K. Nandi. Robust Self-Sparse Fuzzy Clustering for Image Segmentation. IEEE Access. 2020; 8 (99):146182-146195.

Chicago/Turabian Style

Xiaohong Jia; Tao Lei; Xiaogang Du; Shigang Liu; Hongying Meng; Asoke K. Nandi. 2020. "Robust Self-Sparse Fuzzy Clustering for Image Segmentation." IEEE Access 8, no. 99: 146182-146195.

Journal article
Published: 01 August 2020 in Journal of Visual Communication and Image Representation
Reads 0
Downloads 0
ACS Style

Shigang Liu; Yuhong Wang; Xiaosheng Wu; Jun Li; Tao Lei. Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition. Journal of Visual Communication and Image Representation 2020, 71, 1 .

AMA Style

Shigang Liu, Yuhong Wang, Xiaosheng Wu, Jun Li, Tao Lei. Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition. Journal of Visual Communication and Image Representation. 2020; 71 ():1.

Chicago/Turabian Style

Shigang Liu; Yuhong Wang; Xiaosheng Wu; Jun Li; Tao Lei. 2020. "Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition." Journal of Visual Communication and Image Representation 71, no. : 1.

Article
Published: 12 March 2020 in Journal of Mountain Science
Reads 0
Downloads 0

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir (TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree (GBDT), random forest (RF) and information value (InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area, 28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic (ROC) curves, the sensitivity, specificity, overall accuracy (OA), and kappa coefficient (KAPPA). The results showed that the GBDT, RF and InV models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

ACS Style

Tao Chen; Li Zhu; Rui-Qing Niu; C John Trinder; Ling Peng; Tao Lei. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science 2020, 17, 670 -685.

AMA Style

Tao Chen, Li Zhu, Rui-Qing Niu, C John Trinder, Ling Peng, Tao Lei. Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science. 2020; 17 (3):670-685.

Chicago/Turabian Style

Tao Chen; Li Zhu; Rui-Qing Niu; C John Trinder; Ling Peng; Tao Lei. 2020. "Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models." Journal of Mountain Science 17, no. 3: 670-685.

Journal article
Published: 23 July 2019 in IEEE Transactions on Fuzzy Systems
Reads 0
Downloads 0

Clustering algorithms by minimizing an objective function share a clear drawback of having to set the number of clusters manually. Although density peak clustering is able to find the number of clusters, it suffers from memory overflow when it is used for image segmentation because a moderate-size image usually includes a large number of pixels leading to a huge similarity matrix. To address the issue, here we proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. Firstly, the idea of superpixel is used for the density peak (DP) algorithm, which efficiently reduces the size of the similarity matrix and thus improves the computational efficiency of the DP algorithm. Secondly, we employ a density balance algorithm to obtain a robust decision-graph that helps the DP algorithm achieve fully automatic clustering. Finally, a fuzzy c-means clustering based on prior entropy is used in the framework to improve image segmentation results. Because the spatial neighboring information of both the pixels and membership are considered, the final segmentation result is improved effectively. Experiments show that the proposed framework not only achieves automatic image segmentation, but also provides better segmentation results than state-of-the-art algorithms.

ACS Style

Tao Lei; Peng Liu; Xiaohong Jia; Xuande Zhang; Hongying Meng; Asoke K. Nandi. Automatic Fuzzy Clustering Framework for Image Segmentation. IEEE Transactions on Fuzzy Systems 2019, 28, 2078 -2092.

AMA Style

Tao Lei, Peng Liu, Xiaohong Jia, Xuande Zhang, Hongying Meng, Asoke K. Nandi. Automatic Fuzzy Clustering Framework for Image Segmentation. IEEE Transactions on Fuzzy Systems. 2019; 28 (9):2078-2092.

Chicago/Turabian Style

Tao Lei; Peng Liu; Xiaohong Jia; Xuande Zhang; Hongying Meng; Asoke K. Nandi. 2019. "Automatic Fuzzy Clustering Framework for Image Segmentation." IEEE Transactions on Fuzzy Systems 28, no. 9: 2078-2092.

Journal article
Published: 07 June 2019 in IEEE Transactions on Image Processing
Reads 0
Downloads 0

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving meaningful ones. Secondly, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that AMR is useful for improving algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.

ACS Style

Tao Lei; Xiaohong Jia; Tongliang Liu; Shigang Liu; Hongying Meng; Asoke K. Nandi. Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE Transactions on Image Processing 2019, 28, 5510 -5523.

AMA Style

Tao Lei, Xiaohong Jia, Tongliang Liu, Shigang Liu, Hongying Meng, Asoke K. Nandi. Adaptive Morphological Reconstruction for Seeded Image Segmentation. IEEE Transactions on Image Processing. 2019; 28 (11):5510-5523.

Chicago/Turabian Style

Tao Lei; Xiaohong Jia; Tongliang Liu; Shigang Liu; Hongying Meng; Asoke K. Nandi. 2019. "Adaptive Morphological Reconstruction for Seeded Image Segmentation." IEEE Transactions on Image Processing 28, no. 11: 5510-5523.

Journal article
Published: 18 January 2019 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, Precision, Recall, Overall Error, F-score, and Accuracy.

ACS Style

Tao Lei; Yuxiao Zhang; Zhiyong Lv; Shuying Li; Shigang Liu; Asoke K. Nandi. Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters 2019, 16, 982 -986.

AMA Style

Tao Lei, Yuxiao Zhang, Zhiyong Lv, Shuying Li, Shigang Liu, Asoke K. Nandi. Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters. 2019; 16 (6):982-986.

Chicago/Turabian Style

Tao Lei; Yuxiao Zhang; Zhiyong Lv; Shuying Li; Shigang Liu; Asoke K. Nandi. 2019. "Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks." IEEE Geoscience and Remote Sensing Letters 16, no. 6: 982-986.

Journal article
Published: 20 December 2018 in IEEE Transactions on Fuzzy Systems
Reads 0
Downloads 0
ACS Style

Tao Lei; Xiaohong Jia; Yanning Zhang; Shigang Liu; Hongying Meng; Asoke K. Nandi. Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation. IEEE Transactions on Fuzzy Systems 2018, 27, 1753 -1766.

AMA Style

Tao Lei, Xiaohong Jia, Yanning Zhang, Shigang Liu, Hongying Meng, Asoke K. Nandi. Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation. IEEE Transactions on Fuzzy Systems. 2018; 27 (9):1753-1766.

Chicago/Turabian Style

Tao Lei; Xiaohong Jia; Yanning Zhang; Shigang Liu; Hongying Meng; Asoke K. Nandi. 2018. "Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation." IEEE Transactions on Fuzzy Systems 27, no. 9: 1753-1766.

Journal article
Published: 15 November 2018 in Remote Sensing
Reads 0
Downloads 0

To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.

ACS Style

Zhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing 2018, 10, 1809 .

AMA Style

Zhiyong Lv, Tongfei Liu, Jón Atli Benediktsson, Tao Lei, Yiliang Wan. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing. 2018; 10 (11):1809.

Chicago/Turabian Style

Zhiyong Lv; Tongfei Liu; Jón Atli Benediktsson; Tao Lei; Yiliang Wan. 2018. "Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images." Remote Sensing 10, no. 11: 1809.

Journal article
Published: 30 August 2018 in Remote Sensing
Reads 0
Downloads 0

Change detection approaches based on image segmentation are often used for landslide mapping (LM) from very high-resolution (VHR) remote sensing images. However, these approaches usually have two limitations. One is that they are sensitive to thresholds used for image segmentation and require too many parameters. The other one is that the computational complexity of these approaches depends on the image size, and thus they require a long execution time for very high-resolution (VHR) remote sensing images. In this paper, an unsupervised change detection using fast fuzzy c-means clustering (CDFFCM) for LM is proposed. The proposed CDFFCM has two contributions. The first is that we employ a Gaussian pyramid-based fast fuzzy c-means (FCM) clustering algorithm to obtain candidate landslide regions that have a better visual effect due to the utilization of image spatial information. The second is that we use the difference of image structure information instead of grayscale difference to obtain more accurate landslide regions. Three comparative approaches, edge-based level-set (ELSE), region-based level-set (RLSE), and change detection-based Markov random field (CDMRF), and the proposed CDFFCM are evaluated in three true landslide cases in the Lantau area of Hong Kong. The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.

ACS Style

Tao Lei; Dinghua Xue; Zhiyong Lv; Shuying Li; Yanning Zhang; Asoke K. Nandi. Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images. Remote Sensing 2018, 10, 1381 .

AMA Style

Tao Lei, Dinghua Xue, Zhiyong Lv, Shuying Li, Yanning Zhang, Asoke K. Nandi. Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images. Remote Sensing. 2018; 10 (9):1381.

Chicago/Turabian Style

Tao Lei; Dinghua Xue; Zhiyong Lv; Shuying Li; Yanning Zhang; Asoke K. Nandi. 2018. "Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images." Remote Sensing 10, no. 9: 1381.