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Imed Riadh Farah - IEEE Xplore Author Profile

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Author details

Imed Riadh Farah

Also published under: I. R. Farah

Publications

99

Citations

482

Publications by Year

20022024

Co-Authors:

Ali Ben AbbesAzza AbidiM. B. AhmedKhitem AmiriMariem Ayad

Show All Co-Authors (96)

Imed Riadh Farah

Also published under: I. R. Farah

Affiliation

RIADI Laboratory

Manouba School of Engineering

Manouba, Tunisia

Publication Topics

Convolutional Neural Network,
Normalized Difference Vegetation Index,
Deep Learning,
Root Mean Square Error,
Backscattering Coefficient,
Neural Network,
Soil Moisture,
Agricultural Areas,
Deep Learning Models,
Machine Learning,
Soil Moisture Retrieval,
Sustainable Development Goals

Biography

Imed Riadh Farah received the M.D. degree from ISG Institute of Computer Sciences, Tunis, Tunisia, in 1995 and the Dr. Eng. degree from ENSI, Manouba, Tunisia, in 2003.,He was with the Laboratory RIADI, National School of Computer Sciences Engineering, Manouba, Tunisia, where he became a Permanent Researcher in 1995 and a Research Assistant in 1996. Since 2004, he has been an Assistant Professor with the University of Jendouba, Jendouba, Tunisia. His research interest includes image processing, pattern recognition, artificial intelligence, data mining, and their application to remote sensing.,Prof. Farah is a member of Arts-Pi Tunisia.(Based on document published on 9 December 2008).

Publications

99

Citations

482

Publications by Year

20022024

Co-Authors:

Ali Ben Abbes
Azza Abidi
M. B. Ahmed
Khitem Amiri
Mariem Ayad

Show All Co-Authors (96)

Author's Published Works

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Showing 1-25 of 73 results

Journals (38)

Conferences (34)

Early Access Articles (1)

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Xiangzhi Bai(73)
Fugen Zhou(36)
Yu Zhang(8)
Bo Liu(8)
Yuxuan Zhang(7)
Image Processing Center, Beihang University, Beijing, China(36)
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China(22)
Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing, China(19)
Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China(11)
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China(10)
IEEE Access(9)
IEEE Transactions on Fuzzy Systems(5)
IEEE Transactions on Intelligent Transportation Systems(5)
2010 3rd International Congress on Image and Signal Processing(3)
2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)(3)
IEEE(73)
Media(7)
Datasets(1)
Beijing, China(6)
Wollongong, NSW, Australia(3)
Yantai, China(3)
Canberra, ACT, Australia(2)
Paris, France(2)
Infrared Imaging(19)
Image Segmentation(17)
Segmentation Results(17)
Spatial Information(17)
Fuzzy C-means(14)

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Results

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation

Yinuo Wang;Cai Meng;Zhouping Tang;Xiangzhuo Bai;Ping Ji;Xiangzhi Bai

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Early Access Article |

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascular diseases. Different imaging modalities utilize distinct principles to visualize the cerebral vasculature, which leads to the limitations of expensi...Show More

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation

Yinuo Wang;Cai Meng;Zhouping Tang;Xiangzhuo Bai;Ping Ji;Xiangzhi Bai

IEEE Journal of Biomedical and Health Informatics

Year: 2024 | Early Access Article |

Mixed Degradation Image Restoration via Deep Image Prior Empowered by Deep Denoising Engine

Weimin Yuan;Yinuo Wang;Ning Li;Cai Meng;Xiangzhi Bai

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

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Deep Image Prior (DIP) is a powerful unsupervised learning image restoration technique. However, DIP struggles when handling complex degradation scenarios involving mixed image artifacts. To address this limitation, we propose a novel technique to enhance DIP’s performance in handling mixed image degradation. Our method leverages additional deep denoiser, which is deployed as a denoising engine in...Show More

Mixed Degradation Image Restoration via Deep Image Prior Empowered by Deep Denoising Engine

Weimin Yuan;Yinuo Wang;Ning Li;Cai Meng;Xiangzhi Bai

2024 International Joint Conference on Neural Networks (IJCNN)

Year: 2024 | Conference Paper |

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

Yinuo Wang;Kai Chen;Weimin Yuan;Zhouping Tang;Cai Meng;Xiangzhi Bai

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation. Despite the impressive capabilities of SAM on natural scenes, it struggles with performance decline when confronted with medical images, especially those involving blurry boundaries and highly irregular regions of low contrast. In t...Show More

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation

Yinuo Wang;Kai Chen;Weimin Yuan;Zhouping Tang;Cai Meng;Xiangzhi Bai

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2024 | Conference Paper |

Geometry-Augmented Molecular Representation Learning for Property Prediction

Yanan Zhang;Xiangzhi Bai

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2024 | Volume: 21, Issue: 5 | Journal Article |

Cited by: Papers (1)

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Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial information in molecular graphs. For molecular representation learning, most previous GNN methods are specialized in dealing with 2D or 3D molecular data...Show More

Geometry-Augmented Molecular Representation Learning for Property Prediction

Yanan Zhang;Xiangzhi Bai

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2024 | Volume: 21, Issue: 5 | Journal Article |

Atmospheric Transmission and Thermal Inertia Induced Blind Road Segmentation with a Large-Scale Dataset TBRSD

Junzhang Chen;Xiangzhi Bai

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Computer vision-based walking assistants are prominent tools for aiding visually impaired people in navigation. Blind road segmentation is a key element in these walking assistant systems. However, most walking assistant systems rely on visual light images, which is dangerous in weak illumination environments such as darkness or fog. To address this issue and enhance the safety of vision-based wal...Show More

Atmospheric Transmission and Thermal Inertia Induced Blind Road Segmentation with a Large-Scale Dataset TBRSD

Junzhang Chen;Xiangzhi Bai

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework

Yu Zheng;Fugen Zhou;Shangying Liang;Wentao Song;Xiangzhi Bai

IEEE Transactions on Intelligent Transportation Systems

Year: 2023 | Volume: 24, Issue: 12 | Journal Article |

Cited by: Papers (1)

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Video semantic segmentation has achieved great success, which is significant for road scene understanding. However, semantic segmentation remains challenging in poor illumination and inclement weather. Thermal camera, highly invariant to light and highly penetrating to rain and fog, enables semantic segmentation to work under challenging conditions. Thus, this paper explores semantic segmentation ...Show More

Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework

Yu Zheng;Fugen Zhou;Shangying Liang;Wentao Song;Xiangzhi Bai

IEEE Transactions on Intelligent Transportation Systems

Year: 2023 | Volume: 24, Issue: 12 | Journal Article |

Fuzzy Sparse Subspace Clustering for Infrared Image Segmentation

Ying Chen;Zihan Wang;Xiangzhi Bai

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Cited by: Papers (7)

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Infrared image segmentation is a challenging task, due to interference of complex background and appearance inhomogeneity of foreground objects. A critical defect of fuzzy clustering for infrared image segmentation is that the method treats image pixels or fragments in isolation. In this paper, we propose to adopt self-representation from sparse subspace clustering in fuzzy clustering, aiming to i...Show More

Fuzzy Sparse Subspace Clustering for Infrared Image Segmentation

Ying Chen;Zihan Wang;Xiangzhi Bai

IEEE Transactions on Image Processing

Year: 2023 | Volume: 32 | Journal Article |

Receptive-Field and Direction Induced Attention Network for Infrared Dim Small Target Detection With a Large-Scale Dataset IRDST

Heng Sun;Junxiang Bai;Fan Yang;Xiangzhi Bai

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (65)

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Infrared small target detection plays an important role in military and civilian fields while it is difficult to be solved by deep learning (DL) technologies due to scarcity of data and strong interclass imbalance. To relieve scarcity of data, we build a massive dataset IRDST, which contains 142 727 frames. Also, we propose a receptive-field and direction-induced attention network (RDIAN), which i...Show More

Receptive-Field and Direction Induced Attention Network for Infrared Dim Small Target Detection With a Large-Scale Dataset IRDST

Heng Sun;Junxiang Bai;Fan Yang;Xiangzhi Bai

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

A novel continuous beam energy selector for photon intensity-modulated radiotherapy

Xile Zhang;Fugen Zhou;Bo Liu;B.S. Tianyu Xiong;Xiangzhi Bai;Qiuwen Wu

2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

Year: 2022 | Conference Paper |

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A photon synthesis method was developed recently which can generate photon beam of any equivalent energy from only two energies. In this work, a novel continuous beam energy selector for photon intensity-modulated radiotherapy (IMRT) was proposed and validated. Based on the effective path length of the target and organs at risk, a model based on dose reference points was proposed and optimized to ...Show More

A novel continuous beam energy selector for photon intensity-modulated radiotherapy

Xile Zhang;Fugen Zhou;Bo Liu;B.S. Tianyu Xiong;Xiangzhi Bai;Qiuwen Wu

2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

Year: 2022 | Conference Paper |

Light Transport Induced Domain Adaptation for Semantic Segmentation in Thermal Infrared Urban Scenes

Junzhang Chen;Zichao Liu;Darui Jin;Yuanyuan Wang;Fan Yang;Xiangzhi Bai

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 12 | Journal Article |

Cited by: Papers (15)

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Semantic segmentation in urban scenes is widely used in applications of intelligent transportation systems (ITS). In urban scenes, thermal infrared (TIR) images can be captured in weak illumination conditions or in the presence of obscuration (e.g., light fog, smoke). Therefore, TIR images have great potential to endow automated intelligent vehicles or assist navigation systems. However, TIR imagi...Show More

Light Transport Induced Domain Adaptation for Semantic Segmentation in Thermal Infrared Urban Scenes

Junzhang Chen;Zichao Liu;Darui Jin;Yuanyuan Wang;Fan Yang;Xiangzhi Bai

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 12 | Journal Article |

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Ying Chen;Darui Jin;Bin Guo;Xiangzhi Bai

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 12 | Journal Article |

Cited by: Papers (24)

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Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebra...Show More

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Ying Chen;Darui Jin;Bin Guo;Xiangzhi Bai

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 12 | Journal Article |

Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes

Mengyao Lyu;Hu Han;Xiangzhi Bai

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2022 | Volume: 52, Issue: 11 | Journal Article |

Cited by: Papers (2)

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The goal of zero-shot learning (ZSL) is to transfer knowledge learned from seen classes during training to unseen classes for testing, with the help of auxiliary information, such as attributes and descriptions. Most of the existing methods view ZSL as a label-embedding problem, in which class and image representations are embedded to a common space. However, many methods either show a bias toward...Show More

Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes

Mengyao Lyu;Hu Han;Xiangzhi Bai

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2022 | Volume: 52, Issue: 11 | Journal Article |

An Improved Intuitionistic Fuzzy C-Means for Ship Segmentation in Infrared Images

Fan Yang;Zhaoying Liu;Xiangzhi Bai;Yuxuan Zhang

IEEE Transactions on Fuzzy Systems

Year: 2022 | Volume: 30, Issue: 2 | Journal Article |

Cited by: Papers (20)

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Infrared ship segmentation is extensively applied in military fields. Due to noise and intensity inhomogeneity, the segmentation of infrared ship is a challenging task. The fuzzy c-means (FCM) clustering algorithm is widely used in image segmentation. However, traditional FCM is sensitive to noise and unable to obtain desirable segmentation results for infrared ship images. In this article, a nove...Show More

An Improved Intuitionistic Fuzzy C-Means for Ship Segmentation in Infrared Images

Fan Yang;Zhaoying Liu;Xiangzhi Bai;Yuxuan Zhang

IEEE Transactions on Fuzzy Systems

Year: 2022 | Volume: 30, Issue: 2 | Journal Article |

Reconstruction of 3D CT from A Single X-ray Projection View Using CVAE-GAN

Ling Jiang;Mengxi Zhang;Ran Wei;Bo Liu;Xiangzhi Bai;Fugen Zhou

2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE)

Year: 2021 | Conference Paper |

Cited by: Papers (8)

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Computed tomography can provide a 3D view of the patient's internal anatomy. However, traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In order to deal with the impact of organ movement caused by respiration in radiotherapy on the accuracy of radiotherapy, we propose to reco...Show More

Reconstruction of 3D CT from A Single X-ray Projection View Using CVAE-GAN

Ling Jiang;Mengxi Zhang;Ran Wei;Bo Liu;Xiangzhi Bai;Fugen Zhou

2021 IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE)

Year: 2021 | Conference Paper |

Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Darui Jin;Xiangzhi Bai;Yingfan Wang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2021 | Volume: 51, Issue: 7 | Journal Article |

Cited by: Papers (8)

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Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation...Show More

Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Darui Jin;Xiangzhi Bai;Yingfan Wang

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2021 | Volume: 51, Issue: 7 | Journal Article |

Arc Adjacency Matrix-Based Fast Ellipse Detection

Cai Meng;Zhaoxi Li;Xiangzhi Bai;Fugen Zhou

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Cited by: Papers (58)

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Fast and accurate ellipse detection is critical in certain computer vision tasks. In this paper, we propose an arc adjacency matrix-based ellipse detection (AAMED) method to fulfill this requirement. At first, after segmenting the edges into elliptic arcs, the digraph-based arc adjacency matrix (AAM) is constructed to describe their triple sequential adjacency states. Curvature and region constrai...Show More

Arc Adjacency Matrix-Based Fast Ellipse Detection

Cai Meng;Zhaoxi Li;Xiangzhi Bai;Fugen Zhou

IEEE Transactions on Image Processing

Year: 2020 | Volume: 29 | Journal Article |

Global Context Instructive Network for Extreme Crowd Counting

Yuanyuan Wang;Xing Wang;Xiangzhi Bai;Tao Zhao;Yongce Cheng

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (1)

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Crowd counting has gained popularity due to wide applications, such as intelligent security, and urban planning. However, scale variation and perspective distortion make it a challenging task. Most existing works focus on multi-scale feature extraction to address the challenge of scale variation and perspective distortion. In this paper, we propose a novel Global Context Instructive Network (GCINe...Show More

Global Context Instructive Network for Extreme Crowd Counting

Yuanyuan Wang;Xing Wang;Xiangzhi Bai;Tao Zhao;Yongce Cheng

Year: 2020 | Volume: 8 | Journal Article |

Fast Detection of Distant, Infrared Targets in a Single Image Using Multiorder Directional Derivatives

Yanguang Bi;Junzhang Chen;Heng Sun;Xiangzhi Bai

IEEE Transactions on Aerospace and Electronic Systems

Year: 2020 | Volume: 56, Issue: 3 | Journal Article |

Cited by: Papers (16)

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The fast and robust detection of far targets is one of the key techniques in infrared searching and tracking applications. Using multiorder directional derivatives, an effective and concise detection method performed on a single IR image is proposed in this article. First, multiorder directional derivatives of an image are derived from the facet model. According to the derivative characteristics o...Show More

Fast Detection of Distant, Infrared Targets in a Single Image Using Multiorder Directional Derivatives

Yanguang Bi;Junzhang Chen;Heng Sun;Xiangzhi Bai

IEEE Transactions on Aerospace and Electronic Systems

Year: 2020 | Volume: 56, Issue: 3 | Journal Article |

Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

Zichao Liu;Xiangzhi Bai;Haonan Liu;Yuxuan Zhang

IEEE Transactions on Fuzzy Systems

Year: 2020 | Volume: 28, Issue: 9 | Journal Article |

Cited by: Papers (9)

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Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface ...Show More

Multiple-Surface-Approximation-Based FCM With Interval Memberships for Bias Correction and Segmentation of Brain MRI

Zichao Liu;Xiangzhi Bai;Haonan Liu;Yuxuan Zhang

IEEE Transactions on Fuzzy Systems

Year: 2020 | Volume: 28, Issue: 9 | Journal Article |

Distribution Information Based Intuitionistic Fuzzy Clustering for Infrared Ship Segmentation

Darui Jin;Xiangzhi Bai

IEEE Transactions on Fuzzy Systems

Year: 2020 | Volume: 28, Issue: 8 | Journal Article |

Cited by: Papers (22)

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This paper presents a distribution information based intuitionistic fuzzy clustering method for infrared ship segmentation. The algorithm could effectively suppress the influences of nontarget objects with high intensity and intensity inhomogeneity in the infrared ship images. There are mainly two improvements in this paper. First, it proposes a fuzzy clustering algorithm incorporating global dist...Show More

Distribution Information Based Intuitionistic Fuzzy Clustering for Infrared Ship Segmentation

Darui Jin;Xiangzhi Bai

IEEE Transactions on Fuzzy Systems

Year: 2020 | Volume: 28, Issue: 8 | Journal Article |

3D Rigid Registration of Patient Body Surface Point Clouds by Integer Linear Programming

Jingheng Chen;Fugen Zhou;Bo Liu;Xiangzhi Bai;Yuxuan Zhang;Tao Zhao;Nan Li;Yutao Zhou

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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Surface point cloud matching is a useful technique for patient positioning during radiation therapy, system registering of surgical navigation system, etc. A common method for 3D point cloud registration is to estimate the registration function based on the 3D keypoint feature correspondences. However, the feature distance of correct correspondence is usually not the closest, but it is hidden in t...Show More

3D Rigid Registration of Patient Body Surface Point Clouds by Integer Linear Programming

Jingheng Chen;Fugen Zhou;Bo Liu;Xiangzhi Bai;Yuxuan Zhang;Tao Zhao;Nan Li;Yutao Zhou

2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)

Year: 2019 | Conference Paper |

Weighted Schatten P-Norm Minimization with Local and Nonlocal Constraints for Noisy Image Completion

Ruirui Fan;Guangmei Wei;Yuxuan Zhang;Xiangzhi Bai

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | Conference Paper |

Cited by: Papers (3)

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Weighted Schatten p-norm minimization (WSNM) has been used successfully for noisy-free image completion. However, WSNM can introduce extra artifacts if the observed entries of image contain noise. In this paper, we present a novel WSNM-based method for noisy image completion, which incorporates both local smoothness and nonlocal self-similarity in a unified framework. More concretely, the analysis...Show More

Weighted Schatten P-Norm Minimization with Local and Nonlocal Constraints for Noisy Image Completion

Ruirui Fan;Guangmei Wei;Yuxuan Zhang;Xiangzhi Bai

2019 IEEE International Conference on Image Processing (ICIP)

Year: 2019 | Conference Paper |

Bone Age Assessment Based on Rank-Monotonicity Enhanced Ranking CNN

Bo Liu;Yu Zhang;Meicheng Chu;Xiangzhi Bai;Fugen Zhou

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (21)

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Skeletal bone age assessment based on hand x-ray is widely used in many fields. There is an urgent need for automated method to alleviate manual labor and address the problem of intra- and inter-observer variability. Most existing methods modeled the task as regression or multiclass classification problems. However, the regression method over-simplifies the relation between image features and bone...Show More

Bone Age Assessment Based on Rank-Monotonicity Enhanced Ranking CNN

Bo Liu;Yu Zhang;Meicheng Chu;Xiangzhi Bai;Fugen Zhou

Year: 2019 | Volume: 7 | Journal Article |

Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Yu Zheng;Fugen Zhou;Lu Li;Xiangzhi Bai;Changming Sun

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (4)

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Saliency detection is important in computer vision. However, most of the existing saliency models are designed for visible images. It is still a challenging problem to apply saliency detection algorithms on infrared images. In this paper, an effective propagation based saliency detection method for infrared pedestrian images is proposed. Firstly, based on the thermal characteristics of infrared im...Show More

Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Yu Zheng;Fugen Zhou;Lu Li;Xiangzhi Bai;Changming Sun

Year: 2019 | Volume: 7 | Journal Article |

Multiple Organ Localization in CT Image Using Triple-Branch Fully Convolutional Networks

Xuanang Xu;Fugen Zhou;Bo Liu;Xiangzhi Bai

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (9)

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Organ localization is an essential prerequisite for many computed tomography (CT) image analysis tasks such as organ segmentation, lesion detection, and image registration. However, it is a challenging problem due to various causes such as the low-contrast organ boundaries, the large variations of organ appearance, the truncated organs, and the abnormalities. In this work, we propose an automatic ...Show More

Multiple Organ Localization in CT Image Using Triple-Branch Fully Convolutional Networks

Xuanang Xu;Fugen Zhou;Bo Liu;Xiangzhi Bai

Year: 2019 | Volume: 7 | Journal Article |

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