Jing Bai - IEEE Xplore Author Profile

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Author details

Jing Bai

Publications

46

Citations

713

Publications by Year

20112025

Co-Authors:

Mamoun AlazabTalal Ahmed Ali AliZedong AnZheng ChenDaiwu Chen

Show All Co-Authors (101)

Jing Bai

Affiliation

School of Information Science and Technology

Shijiazhuang Tiedao University

Shijiazhuang, China

Publication Topics

Attention Mechanism,
Convolutional Neural Network,
Decoding Algorithm,
Deep Learning,
Low-density Parity-check,
Neural Network,
Additive Noise,
Binary Low-density Parity-check Codes,
Classification Accuracy,
Classification Performance,
Decoding Performance,
Low-density Parity-check Codes

Biography

Jing Bai (Senior Member, IEEE) received the B.S. degree in electronic and information engineering from Zhengzhou University, Zhengzhou, China, in 2004, and the Ph.D. degree in pattern recognition and intelligent systems from Xidian University, Xi'an, China, in 2009.,She is currently a Professor with Xidian University. Her research interests include image processing, machine learning, and intelligent information processing.(Based on document published on 22 February 2023).

Publications

46

Citations

713

Publications by Year

20112025

Co-Authors:

Mamoun Alazab
Talal Ahmed Ali Ali
Zedong An
Zheng Chen
Daiwu Chen

Show All Co-Authors (101)

Author's Published Works

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Showing 1-25 of 47 results

Conferences (30)

Journals (16)

Magazines (1)

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Lingfeng Wang(46)
Chunhong Pan(44)
Shiming Xiang(21)
Gaofeng Meng(9)
Huaiyu Wu(6)
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China(8)
National Laboratory of Pattern Recognition, Chinese Academy of Sciences(6)
Department of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China(5)
NLPR, Chinese Academy of Sciences(5)
NLPR, Institute of Automation, Chinese Academy of Sciences(3)
2017 IEEE International Conference on Image Processing (ICIP)(5)
2015 IEEE International Conference on Image Processing (ICIP)(4)
IEEE Transactions on Circuits and Systems for Video Technology(3)
IEEE Transactions on Neural Networks and Learning Systems(3)
2011 18th IEEE International Conference on Image Processing(2)
IEEE(47)
Media(1)
Beijing, China(6)
Quebec City, QC, Canada(4)
Athens, Greece(2)
Brussels, Belgium(2)
New Orleans, LA, USA(2)
Convolutional Neural Network(12)
Image Segmentation(8)
Objective Function(8)
Segmentation Results(8)
Energy Function(7)

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Results

TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers

Tai An;Xin Zhang;Chunlei Huo;Bin Xue;Lingfeng Wang;Chunhong Pan

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2022 | Volume: 15 | Journal Article |

Cited by: Papers (36)

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Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on d...Show More

TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers

Tai An;Xin Zhang;Chunlei Huo;Bin Xue;Lingfeng Wang;Chunhong Pan

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2022 | Volume: 15 | Journal Article |

Few-Shot Learning via Feature Hallucination with Variational Inference

Qinxuan Luo;Lingfeng Wang;Jingguo Lv;Shiming Xiang;Chunhong Pan

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

Year: 2021 | Conference Paper |

Cited by: Papers (29)

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Deep learning has achieved huge success in the field of artificial intelligence, but the performance heavily depends on labeled data. Few-shot learning aims to make a model rapidly adapt to unseen classes with few labeled samples after training on a base dataset, and this is useful for tasks lacking labeled data such as medical image processing. Considering that the core problem of few-shot learni...Show More

Few-Shot Learning via Feature Hallucination with Variational Inference

Qinxuan Luo;Lingfeng Wang;Jingguo Lv;Shiming Xiang;Chunhong Pan

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

Year: 2021 | Conference Paper |

Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation

Binjie Mao;Lingfeng Wang;Shiming Xiang;Chunhong Pan

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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Few shot segmentation is a newly-developing and challenging computer vision task which is only provided with few labeled samples of the novel class. Some recent works on this problem focus more on how to design an effective comparison module but ignore how to extract the features passed to compare. In this paper we propose a novel model named LTAF-Net for few-shot segmentation. This model aims to ...Show More

Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation

Binjie Mao;Lingfeng Wang;Shiming Xiang;Chunhong Pan

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2021 | Conference Paper |

Local-Aggregation Graph Networks

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Qi Zhang;Shiming Xiang;Chunhong Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 11 | Journal Article |

Cited by: Papers (10)

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Convolutional neural networks (CNNs) provide a dramatically powerful class of models, but are subject to traditional convolution that can merely aggregate permutation-ordered and dimension-equal local inputs. It causes that CNNs are allowed to only manage signals on Euclidean or grid-like domains (e.g., images), not ones on non-Euclidean or graph domains (e.g., traffic networks). To eliminate this...Show More

Local-Aggregation Graph Networks

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Qi Zhang;Shiming Xiang;Chunhong Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 11 | Journal Article |

Deep Self-Evolution Clustering

Jianlong Chang;Gaofeng Meng;Lingfeng Wang;Shiming Xiang;Chunhong Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 4 | Journal Article |

Cited by: Papers (61)

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Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is ...Show More

Deep Self-Evolution Clustering

Jianlong Chang;Gaofeng Meng;Lingfeng Wang;Shiming Xiang;Chunhong Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2020 | Volume: 42, Issue: 4 | Journal Article |

Deep Hierarchical Encoder–Decoder Network for Image Captioning

Xinyu Xiao;Lingfeng Wang;Kun Ding;Shiming Xiang;Chunhong Pan

IEEE Transactions on Multimedia

Year: 2019 | Volume: 21, Issue: 11 | Journal Article |

Cited by: Papers (73)

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Encoder-decoder models have been widely used in image captioning, and most of them are designed via single long short term memory (LSTM). The capacity of single-layer network, whose encoder and decoder are integrated together, is limited for such a complex task of image captioning. Moreover, how to effectively increase the “vertical depth” of encoder-decoder remains to be solved. To deal with thes...Show More

Deep Hierarchical Encoder–Decoder Network for Image Captioning

Xinyu Xiao;Lingfeng Wang;Kun Ding;Shiming Xiang;Chunhong Pan

IEEE Transactions on Multimedia

Year: 2019 | Volume: 21, Issue: 11 | Journal Article |

Weakly Semantic Guided Action Recognition

Tingzhao Yu;Lingfeng Wang;Cheng Da;Huxiang Gu;Shiming Xiang;Chunhong Pan

IEEE Transactions on Multimedia

Year: 2019 | Volume: 21, Issue: 10 | Journal Article |

Cited by: Papers (26)

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Action recognition plays a fundamental role in computer vision and video analysis. Nevertheless, extracting effective spatial-temporal features remains a challenging task. This paper proposes three simple but effective weakly semantic guided modules (SGMs) for both environment-constrained and cross-domain action recognition. The SGMs are composed of total 3-D convolution and element-wise gated ope...Show More

Weakly Semantic Guided Action Recognition

Tingzhao Yu;Lingfeng Wang;Cheng Da;Huxiang Gu;Shiming Xiang;Chunhong Pan

IEEE Transactions on Multimedia

Year: 2019 | Volume: 21, Issue: 10 | Journal Article |

Adaptation of Alpha-Beta Search Algorithm for Real-Time Strategy Game

Sijia Chen;Hongping Yan;Lingfeng Wang

2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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Real-time strategy(RTS)[1] game is known for its large action space, rapid response speed, and subtle game scenarios, and it has proven to be a very challenging application area in artificial intelligence research. In the RTS game, the search time increases exponentially as the number of units increases. So it is not possible to achieve a complete search for the game tree in the strict circumstanc...Show More

Adaptation of Alpha-Beta Search Algorithm for Real-Time Strategy Game

Sijia Chen;Hongping Yan;Lingfeng Wang

2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)

Year: 2018 | Conference Paper |

Fast Variational Level Set Based Image Segmentation via Two-Scale Filtering Model

Lingfeng Wang;Ying Wang;Chunhong Pan

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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One major difficulty in medical image segmentation is intensity inhomogeneity, which manifests itself with a slow intensity variation over the whole image domain. Recently, a local binary fitting (LBF) model has been proposed to solve this problem within level set segmentation framework. However, the LBF model has two main problems, i.e., high computational cost and sensitivity to initialization. ...Show More

Fast Variational Level Set Based Image Segmentation via Two-Scale Filtering Model

Lingfeng Wang;Ying Wang;Chunhong Pan

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2018 | Conference Paper |

Reconstructed Densenets for Image Super-Resolution

Lingfeng Wang;Linwei Qiu;Wei Sui;Chunhong Pan

2018 25th IEEE International Conference on Image Processing (ICIP)

Year: 2018 | Conference Paper |

Cited by: Papers (2)

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Deep learning has been successfully applied to single image super-resolution problem due to its high data fitting ability. However, the trending of deeper layers and wider receptive field to acquire better performance brings high computation complexity and serious information vanishing. To address this problem, we proposed a new Reconstructed DenseNets model for super-resolution. The basic idea be...Show More

Reconstructed Densenets for Image Super-Resolution

Lingfeng Wang;Linwei Qiu;Wei Sui;Chunhong Pan

2018 25th IEEE International Conference on Image Processing (ICIP)

Year: 2018 | Conference Paper |

Two-Stream Designed 2D/3D Residual Networks with Lstms for Action Recognition in Videos

Lifei Song;Liguo Weng;Lingfeng Wang;Xia Min;Chunhong Pan

2018 25th IEEE International Conference on Image Processing (ICIP)

Year: 2018 | Conference Paper |

Cited by: Papers (9)

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Convolutional Neural Networks(CNNs) have achieved great success for object recognition in still images. However, CNNs can't make evident improvement for action recognition in videos, one reason is that many current network architectures are relatively shallow compared with deep models in image domain, and the other reason is that CNNs can't capture effective long-term motion information from video...Show More

Two-Stream Designed 2D/3D Residual Networks with Lstms for Action Recognition in Videos

Lifei Song;Liguo Weng;Lingfeng Wang;Xia Min;Chunhong Pan

2018 25th IEEE International Conference on Image Processing (ICIP)

Year: 2018 | Conference Paper |

Self-Paced AutoEncoder

Tingzhao Yu;Chaoxu Guo;Lingfeng Wang;Shiming Xiang;Chunhong Pan

IEEE Signal Processing Letters

Year: 2018 | Volume: 25, Issue: 7 | Journal Article |

Cited by: Papers (18)

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Autoencoder, which learns latent representations of samples in an unsupervised manner, has great potential in computer vision and signal processing. However, the diversity of samples makes learning a component autoencoder remaining a challenging task. This letter proposes a novel Self-Paced AutoEncoder (SPAE) for unsupervised feature extraction. The motivation behind this letter is to take samples...Show More

Self-Paced AutoEncoder

Tingzhao Yu;Chaoxu Guo;Lingfeng Wang;Shiming Xiang;Chunhong Pan

IEEE Signal Processing Letters

Year: 2018 | Volume: 25, Issue: 7 | Journal Article |

Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan

IEEE Intelligent Transportation Systems Magazine

Year: 2018 | Volume: 10, Issue: 2 | Magazine Article |

Cited by: Papers (39)

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Due to the factors such as visual occlusion, illumination change and pose variation, it is a challenging task to develop effective and efficient models for vehicle detection and classification in surveillance videos. Although plenty of existing related models have been proposed, many issues still need to be resolved. Typically, vehicle detection and classification methods should be vulnerable in c...Show More

Vision-Based Occlusion Handling and Vehicle Classification for Traffic Surveillance Systems

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan

IEEE Intelligent Transportation Systems Magazine

Year: 2018 | Volume: 10, Issue: 2 | Magazine Article |

Groupwise Retargeted Least-Squares Regression

Lingfeng Wang;Chunhong Pan

IEEE Transactions on Neural Networks and Learning Systems

Year: 2018 | Volume: 29, Issue: 4 | Journal Article |

Cited by: Papers (33)

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In this brief, we propose a new groupwise retargeted least squares regression (GReLSR) model for multicategory classification. The main motivation behind GReLSR is to utilize an additional regularization to restrict the translation values of ReLSR, so that they should be similar within same class. By analyzing the regression targets of ReLSR, we propose a new formulation of ReLSR, where the transl...Show More

Groupwise Retargeted Least-Squares Regression

Lingfeng Wang;Chunhong Pan

IEEE Transactions on Neural Networks and Learning Systems

Year: 2018 | Volume: 29, Issue: 4 | Journal Article |

Learnable contextual regularization for semantic segmentation of indoor scene images

Jun Chu;Xu Xiao;Gaofeng Meng;Lingfeng Wang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

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Semantic segmentation of indoor scene images has a wide range of applications. However, due to a large number of classes and uneven distribution in indoor scenes, mislabels are often made when facing small objects or boundary regions. Technically, contextual information may benefit for segmentation results, but has not yet been exploited sufficiently. In this paper, we propose a learnable contextu...Show More

Learnable contextual regularization for semantic segmentation of indoor scene images

Jun Chu;Xu Xiao;Gaofeng Meng;Lingfeng Wang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cascaded temporal spatial features for video action recognition

Tingzhao Yu;Huxiang Gu;Lingfeng Wang;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cited by: Papers (8)

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Extracting spatial-temporal descriptors is a challenging task for video-based human action recognition. We decouple the 3D volume of video frames directly into a cascaded temporal spatial domain via a new convolutional architecture. The motivation behind this design is to achieve deep nonlinear feature representations with reduced network parameters. First, a 1D temporal network with shared parame...Show More

Cascaded temporal spatial features for video action recognition

Tingzhao Yu;Huxiang Gu;Lingfeng Wang;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Image super-resolution via deep dilated convolutional networks

Zehao Huang;Lingfeng Wang;Gaofeng Meng;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cited by: Papers (8)

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Deep learning techniques have been successfully applied in single image super-resolution (SR). Recently, researches have shown that increasing the depth of network can significantly improve SR performance. Very deep networks for SR achieved a large improvement than former methods. However, simply increasing depths basically introduce more parameters and this lead to cumbersome computational cost. ...Show More

Image super-resolution via deep dilated convolutional networks

Zehao Huang;Lingfeng Wang;Gaofeng Meng;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Context-aware cascade network for semantic labeling in VHR image

Yongcheng Liu;Bin Fan;Lingfeng Wang;Jun Bai;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cited by: Papers (4)

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Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex manmade objects with different materials and fine-structured objects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-to-end network for semantic labeling. Specifically, our network not only improves the labeling accuracy of...Show More

Context-aware cascade network for semantic labeling in VHR image

Yongcheng Liu;Bin Fan;Lingfeng Wang;Jun Bai;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

MR images segmentation and bias correction via LIC model

Lingfeng Wang;Jie Huang;Bin Lav;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cited by: Papers (1)

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This paper presents a novel Linear Intrinsic Component (LIC) model for simultaneous estimation of bias field and segmentation of magnetic resonance (MR) images with the intensity inhomogene-ity. The core of LIC model is linear transformation, which is derived from Taylor expansion of non-linear model. Due to the linear transformation, observed image can be decomposed into four components, namely, ...Show More

MR images segmentation and bias correction via LIC model

Lingfeng Wang;Jie Huang;Bin Lav;Chunhong Pan

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Deep Adaptive Image Clustering

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

Cited by: Papers (271)

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Image clustering is a crucial but challenging task in machine learning and computer vision. Existing methods often ignore the combination between feature learning and clustering. To tackle this problem, we propose Deep Adaptive Clustering (DAC) that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters. In DAC, t...Show More

Deep Adaptive Image Clustering

Jianlong Chang;Lingfeng Wang;Gaofeng Meng;Shiming Xiang;Chunhong Pan

2017 IEEE International Conference on Computer Vision (ICCV)

Year: 2017 | Conference Paper |

RoDLSR: Robust discriminative least squares regression model for multi-category classification

Lingfeng Wang;Shuaizheng Liu;Chunhong Pan

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Cited by: Papers (7)

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Discriminative least squares regression (DLSR) is a simple yet effective method for multi-class classification. One problem of DLSR is that it is lack of robustness to outliers. In order to tackle this difficulty, in this paper, we propose a novel Robust DLSR (RoDLSR) model. The core idea behind RoDLSR is to find and further ignore the outliers among the support vector set. Specifically, we modify...Show More

RoDLSR: Robust discriminative least squares regression model for multi-category classification

Lingfeng Wang;Shuaizheng Liu;Chunhong Pan

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Learning deep vector regression model for no-reference image quality assessment

Jie Gu;Gaofeng Meng;Lingfeng Wang;Chunhong Pan

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Cited by: Papers (1)

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The goal of no-reference image quality assessment (NR-IQA) is to estimate human perceived image quality without access to either reference image or prior knowledge about distortion type. Previous approaches for this problem are typically based on a regression framework that maps the image features directly to a quality score. In contrast, psychological evidence shows that humans prefer to evaluate...Show More

Learning deep vector regression model for no-reference image quality assessment

Jie Gu;Gaofeng Meng;Lingfeng Wang;Chunhong Pan

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2017 | Conference Paper |

Fine-structured object segmentation via edge-guided graph cut with interaction simplification

Yongchao Gong;Shiming Xiang;Lingfeng Wang;Chunhong Pan

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2016 | Conference Paper |

Cited by: Papers (2)

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Fine-structured object segmentation is a challenging problem in object segmentation community. There are mainly two difficulties that can seriously degrade the segmentation quality: 1) insufficient interactions on fine structures due to the high demand of time and manual efforts, and 2) shrinking bias that discourages long object boundaries. To address these two issues, we develop a novel method w...Show More

Fine-structured object segmentation via edge-guided graph cut with interaction simplification

Yongchao Gong;Shiming Xiang;Lingfeng Wang;Chunhong Pan

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2016 | Conference Paper |

Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction

Wei Sui;Lingfeng Wang;Bin Fan;Hongfei Xiao;Huaiyu Wu;Chunhong Pan

IEEE Transactions on Visualization and Computer Graphics

Year: 2016 | Volume: 22, Issue: 3 | Journal Article |

Cited by: Papers (20) | Patents (2)

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Urban building reconstruction is an important step for urban digitization and realisticvisualization. In this paper, we propose a novel automatic method to recover urban building geometry from 3D point clouds. The proposed method is suitable for buildings composed of planar polygons and aligned with the gravity direction, which are quite common in the city. Our key observation is that the building...Show More

Layer-Wise Floorplan Extraction for Automatic Urban Building Reconstruction

Wei Sui;Lingfeng Wang;Bin Fan;Hongfei Xiao;Huaiyu Wu;Chunhong Pan

IEEE Transactions on Visualization and Computer Graphics

Year: 2016 | Volume: 22, Issue: 3 | Journal Article |

MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification

Lingfeng Wang;Xu-Yao Zhang;Chunhong Pan

IEEE Transactions on Neural Networks and Learning Systems

Year: 2016 | Volume: 27, Issue: 12 | Journal Article |

Cited by: Papers (31)

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In this brief, we propose a new margin scalable discriminative least squares regression (MSDLSR) model for multicategory classification. The main motivation behind the MSDLSR is to explicitly control the margin of DLSR model. We first prove that the DLSR is a relaxation of the traditional L2-support vector machine. Based on this fact, we further provide a theorem on the margin of DLSR. With this t...Show More

MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification

Lingfeng Wang;Xu-Yao Zhang;Chunhong Pan

IEEE Transactions on Neural Networks and Learning Systems

Year: 2016 | Volume: 27, Issue: 12 | Journal Article |

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