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Yuting Fang - IEEE Xplore Author Profile

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Author details

Yuting Fang

Publications

24

Citations

153

Publications by Year

20162024

Co-Authors:

Saman AtapattuYonghao ChenPrathapasinghe DharmawansaZhiguo DingAndrew W. Eckford

Show All Co-Authors (30)

Yuting Fang

Affiliation

Department of Electrical and Electronic Engineering

University of Melbourne

Victoria, Australia

Publication Topics

Numerical Results,
Bit Error Rate,
Molecular Communication,
Bit Error,
Channel Estimation,
Channel Impulse Response,
Channel Response,
Number Of Molecules,
Perfect Channel State Information,
Propagation Environment,
User Pairing,
Wireless Networks

Biography

Yuting Fang (Member, IEEE) received the first B.Eng. degree from the Beijing University of Posts and Telecommunications, China, the second B.Eng. degree (Hons.) from the Queen Mary University of London, U.K., in 2014, and the Ph.D. degree from the Australian National University, Australia, in 2020. She is currently a Doreen Thomas Postdoctoral Fellow with the Department of Electrical and Electronic Engineering, The University of Melbourne. Her research interests include modeling communication networks and machine learning. She has received several prestigious awards and project research funds, including a University Postdoctoral Fellowship and an Australian Research Council Discovery Project.(Based on document published on 2 February 2023).

Publications

24

Citations

153

Publications by Year

20162024

Co-Authors:

Saman Atapattu
Yonghao Chen
Prathapasinghe Dharmawansa
Zhiguo Ding
Andrew W. Eckford

Show All Co-Authors (30)

Author's Published Works

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Showing 1-25 of 4,478 resultsfor "Index Terms":Confidence Map

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Results

Inferential Clustering Approach for Microarray Experiments with Replicated Measurements

Miquel Salicru;Sergi Vives;Tian Zheng

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2009 | Volume: 6, Issue: 4 | Journal Article |

Cited by: Papers (6)

Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a general clustering approach based on the confidence interval inferential meth...Show More

Inferential Clustering Approach for Microarray Experiments with Replicated Measurements

Miquel Salicru;Sergi Vives;Tian Zheng

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2009 | Volume: 6, Issue: 4 | Journal Article |

Feature Augmentation for Learning Confidence Measure in Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Image Processing

Year: 2017 | Volume: 26, Issue: 12 | Journal Article |

Cited by: Papers (30)

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Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial c...Show More

Feature Augmentation for Learning Confidence Measure in Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Image Processing

Year: 2017 | Volume: 26, Issue: 12 | Journal Article |

Color-based road detection and its evaluation on the KITTI road benchmark

B. Wang;V. Frémont;S. A. Rodríguez

2014 IEEE Intelligent Vehicles Symposium Proceedings

Year: 2014 | Conference Paper |

Cited by: Papers (50)

Road detection is one of the key issues of scene understanding for Advanced Driving Assistance Systems (ADAS). Recent approaches has addressed this issue through the use of different kinds of sensors, features and algorithms. KITTI-ROAD benchmark has provided an open-access dataset and standard evaluation mean for road area detection. In this paper, we propose an improved road detection algorithm ...Show More

Color-based road detection and its evaluation on the KITTI road benchmark

B. Wang;V. Frémont;S. A. Rodríguez

2014 IEEE Intelligent Vehicles Symposium Proceedings

Year: 2014 | Conference Paper |

Estimating the 2D Static Map Based on Moving Stereo Camera

Shadi M. Saleh;Sinan A. Khwandah;Wolfram Hardt;Marcus Hilbrich;Pavlos I. Lazaridis

2018 24th International Conference on Automation and Computing (ICAC)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

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Perception is an essential procedure for intelligent vehicles where the safety issue is the most critical one. Usually, the perceptual approach is constructed based on measurements received from multiple sensors such as (Radar, and LiDAR) in order to model the immediate driving environment for autonomous vehicles navigation. These sensors are often limited and uncertain in providing visual informa...Show More

Estimating the 2D Static Map Based on Moving Stereo Camera

Shadi M. Saleh;Sinan A. Khwandah;Wolfram Hardt;Marcus Hilbrich;Pavlos I. Lazaridis

2018 24th International Conference on Automation and Computing (ICAC)

Year: 2018 | Conference Paper |

Fusion of ALOS-2/PALSAR-2 and Sentinel-1 Polarimetric Imagery with Explainable Neural Network-based Confidence Level Estimation for Glacier Surface Classification

Gunjan Joshi;Ryo Natsuaki;Akira Hirose

2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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Glacier monitoring and classification play a vital role in understanding the impact of climate change. Remote sensing imagery, particularly synthetic aperture radar (SAR) data, offers valuable information for glacier surface classification due to its all-weather and day-and-night imaging capability. This paper proposes an approach that combines dual-pol ALOS-2/PALSAR-2 and dual-pol Sentinel-1 pola...Show More

Fusion of ALOS-2/PALSAR-2 and Sentinel-1 Polarimetric Imagery with Explainable Neural Network-based Confidence Level Estimation for Glacier Surface Classification

Gunjan Joshi;Ryo Natsuaki;Akira Hirose

2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

Year: 2023 | Conference Paper |

Distributed collaborative situation-map making for disaster response

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Interacting with Computers

Year: 2011 | Volume: 23, Issue: 4 | Journal Article |

Cited by: Papers (2)

A situation map that shows the overview of a disaster situation serves as a valuable tool for disaster response teams. It helps them to orientate their location and to make disaster response decisions. It is, however, a complicated task to rapidly generate a complete and comprehensive situation map of a disaster area, particularly due to the centralized organization of disaster management and the ...Show More

Distributed collaborative situation-map making for disaster response

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Interacting with Computers

Year: 2011 | Volume: 23, Issue: 4 | Journal Article |

Unified Confidence Estimation Networks for Robust Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Image Processing

Year: 2019 | Volume: 28, Issue: 3 | Journal Article |

Cited by: Papers (35)

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We present a deep architecture that estimates a stereo confidence, which is essential for improving the accuracy of stereo matching algorithms. In contrast to existing methods based on deep convolutional neural networks (CNNs) that rely on only one of the matching cost volume or estimated disparity map, our network estimates the stereo confidence by using the two heterogeneous inputs simultaneousl...Show More

Unified Confidence Estimation Networks for Robust Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Image Processing

Year: 2019 | Volume: 28, Issue: 3 | Journal Article |

Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization

Péter Fankhauser;Michael Bloesch;Marco Hutter

IEEE Robotics and Automation Letters

Year: 2018 | Volume: 3, Issue: 4 | Journal Article |

Cited by: Papers (183)

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Mobile robots build on accurate, real-time mapping with onboard range sensors to achieve autonomous navigation over rough terrain. Existing approaches often rely on absolute localization based on tracking of external geometric or visual features. To circumvent the reliability issues of these approaches, we propose a novel terrain mapping method, which bases on proprioceptive localization from kine...Show More

Probabilistic Terrain Mapping for Mobile Robots With Uncertain Localization

Péter Fankhauser;Michael Bloesch;Marco Hutter

IEEE Robotics and Automation Letters

Year: 2018 | Volume: 3, Issue: 4 | Journal Article |

Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion

Runmin Cong;Jianjun Lei;Changqing Zhang;Qingming Huang;Xiaochun Cao;Chunping Hou

IEEE Signal Processing Letters

Year: 2016 | Volume: 23, Issue: 6 | Journal Article |

Cited by: Papers (198)

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Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. First, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poo...Show More

Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion

Runmin Cong;Jianjun Lei;Changqing Zhang;Qingming Huang;Xiaochun Cao;Chunping Hou

IEEE Signal Processing Letters

Year: 2016 | Volume: 23, Issue: 6 | Journal Article |

The Effect of Color Scales on Climate Scientists’ Objective and Subjective Performance in Spatial Data Analysis Tasks

Aritra Dasgupta;Jorge Poco;Bernice Rogowitz;Kyungsik Han;Enrico Bertini;Cláudio T. Silva

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 3 | Journal Article |

Cited by: Papers (20)

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Geographical maps encoded with rainbow color scales are widely used by climate scientists. Despite a plethora of evidence from the visualization and vision sciences literature about the shortcomings of the rainbow color scale, they continue to be preferred over perceptually optimal alternatives. To study and analyze this mismatch between theory and practice, we present a web-based user study that ...Show More

The Effect of Color Scales on Climate Scientists’ Objective and Subjective Performance in Spatial Data Analysis Tasks

Aritra Dasgupta;Jorge Poco;Bernice Rogowitz;Kyungsik Han;Enrico Bertini;Cláudio T. Silva

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 3 | Journal Article |

A contour extraction method of coke optical texture based on edge confidence map

Peizhen Wang;Jialong Miao;Fang Zhou;Dailin Zhang

2012 24th Chinese Control and Decision Conference (CCDC)

Year: 2012 | Conference Paper |

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Contour information is an important feature in recognition of coke microscopic optical texture. In view of the characteristic of coke microscopic, a contour extraction method, which is based on joint edge confidence map and dual threshold, is proposed. At first, an edge confidence map with gradient magnitude and orientation is built, then non-maximum suppression is employed to obtain local externa...Show More

A contour extraction method of coke optical texture based on edge confidence map

Peizhen Wang;Jialong Miao;Fang Zhou;Dailin Zhang

2012 24th Chinese Control and Decision Conference (CCDC)

Year: 2012 | Conference Paper |

People-background segmentation with unequal error cost

Álvaro García-Martín;Andrea Cavallaro;José M. Martínez

2012 19th IEEE International Conference on Image Processing

Year: 2012 | Conference Paper |

Cited by: Papers (2)

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We address the problem of segmenting a video in two classes of different semantic value, namely background and people, with the goal of guaranteeing that no people (or body parts) are classified as background. Body parts classified as background are given a higher classification error cost (segmentation with bias on background), as opposed to traditional approaches focused on people detection. To ...Show More

People-background segmentation with unequal error cost

Álvaro García-Martín;Andrea Cavallaro;José M. Martínez

2012 19th IEEE International Conference on Image Processing

Year: 2012 | Conference Paper |

Deep stereo confidence prediction for depth estimation

Sunok Kim;Dongbo Min;Bumsub Ham;Seungryong Kim;Kwanghoon Sohn

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Cited by: Papers (21)

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We present a novel method that predicts a confidence to improve the accuracy of an estimated depth map in stereo matching. In contrast to existing learning based approaches relying on hand-crafted confidence features, we cast this problem into a convolutional neural network, learned using both a matching cost volume and its associated disparity map. As the size of the matching cost volume varies d...Show More

Deep stereo confidence prediction for depth estimation

Sunok Kim;Dongbo Min;Bumsub Ham;Seungryong Kim;Kwanghoon Sohn

2017 IEEE International Conference on Image Processing (ICIP)

Year: 2017 | Conference Paper |

Reasoning Support for Mapping Revision

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Journal of Logic and Computation

Year: 2009 | Volume: 19, Issue: 5 | Journal Article |

Cited by: Papers (3)

Finding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a ma...Show More

Reasoning Support for Mapping Revision

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Journal of Logic and Computation

Year: 2009 | Volume: 19, Issue: 5 | Journal Article |

Real-time image recovery using temporal image fusion

André Mora;José Manuel Fonseca;Rita Ribeiro

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Year: 2013 | Conference Paper |

Cited by: Papers (1)

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In computer vision systems an unpredictable image corruption can have significant impact on its usability. Image recovery methods for partial image damage, in particular in moving scenarios, can be crucial for recovering corrupted images. In these situations, image fusion techniques can be successfully applied to congregate information taken at different instants and from different points-of-view ...Show More

Real-time image recovery using temporal image fusion

André Mora;José Manuel Fonseca;Rita Ribeiro

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Year: 2013 | Conference Paper |

Efficient stereo matching based on a new confidence metric

Won-Hee Lee;Yumi Kim;Jong Beom Ra

2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)

Year: 2012 | Conference Paper |

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In this paper, we propose a new confidence metric for efficient stereo matching. To measure the confidence of a stereo match, we refer to the curvatures around the two minimum costs of a cost curve, the size of aggregation kernel, and the occlusion information. Using the proposed confidence metric, we then design a weighted median filter, in order to refine the initially estimated disparities with...Show More

Efficient stereo matching based on a new confidence metric

Won-Hee Lee;Yumi Kim;Jong Beom Ra

2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)

Year: 2012 | Conference Paper |

Fast Depth Prediction and Obstacle Avoidance on a Monocular Drone Using Probabilistic Convolutional Neural Network

Xin Yang;Jingyu Chen;Yuanjie Dang;Hongcheng Luo;Yuesheng Tang;Chunyuan Liao;Peng Chen;Kwang-Ting Cheng

IEEE Transactions on Intelligent Transportation Systems

Year: 2021 | Volume: 22, Issue: 1 | Journal Article |

Cited by: Papers (49)

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Recent studies employ advanced deep convolutional neural networks (CNNs) for monocular depth perception, which can hardly run efficiently on small drones that rely on low/middle-grade GPU(e.g. TX2 and 1050Ti) for computation. In addition, the methods which can effectively and efficiently produce probabilistic depth prediction with a measure of model confidence have not been well studied. The lack ...Show More

Fast Depth Prediction and Obstacle Avoidance on a Monocular Drone Using Probabilistic Convolutional Neural Network

Xin Yang;Jingyu Chen;Yuanjie Dang;Hongcheng Luo;Yuesheng Tang;Chunyuan Liao;Peng Chen;Kwang-Ting Cheng

IEEE Transactions on Intelligent Transportation Systems

Year: 2021 | Volume: 22, Issue: 1 | Journal Article |

Simultaneous Localization and Mapping: A Pseudolinear Kalman Filter (PLKF) Approach

Chandima Dedduwa Pathiranage;Keigo Watanabe;Buddhika Jayasekara;Kiyotaka Izumi

2008 4th International Conference on Information and Automation for Sustainability

Year: 2008 | Conference Paper |

Cited by: Papers (4)

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This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is ...Show More

Simultaneous Localization and Mapping: A Pseudolinear Kalman Filter (PLKF) Approach

Chandima Dedduwa Pathiranage;Keigo Watanabe;Buddhika Jayasekara;Kiyotaka Izumi

2008 4th International Conference on Information and Automation for Sustainability

Year: 2008 | Conference Paper |

Real time confidence estimation and multi-scale camouflage object detection

Xufei Wang;Xiaokang Ren

2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

Year: 2023 | Conference Paper |

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Due to the fact that objects with camouflage blend seamlessly into their surroundings, exhibiting a exceptional resemblance in terms of texture, color, shape, etc., and possessing boundaries that are indistinguishable from their environment, the detection of such camouflaged objects becomes significantly more challenging. In this research, we introduce a novel network called CRNet, which incorpora...Show More

Real time confidence estimation and multi-scale camouflage object detection

Xufei Wang;Xiaokang Ren

2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

Year: 2023 | Conference Paper |

Robust Object Segmentation using Adaptive Thresholding

Xiaxi Huang;Nikolaos V. Boulgouris

2007 IEEE International Conference on Image Processing

Year: 2007 | Conference Paper |

Cited by: Papers (5) | Patents (1)

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This paper proposes a robust object segmentation algorithm that tackles problems that arise in environments in which the foreground and background colours are similar and there is light reflection in the shadow areas. The proposed algorithm bases its efficiency on a novel RGB colour detection process with adaptive threshold and edge detection, which are combined in order to obtain a foreground map...Show More

Robust Object Segmentation using Adaptive Thresholding

Xiaxi Huang;Nikolaos V. Boulgouris

2007 IEEE International Conference on Image Processing

Year: 2007 | Volume: 1 | Conference Paper |

Adversarial Confidence Estimation Networks for Robust Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Intelligent Transportation Systems

Year: 2021 | Volume: 22, Issue: 11 | Journal Article |

Cited by: Papers (6)

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Stereo matching aiming to perceive the 3-D geometry of a scene facilitates numerous computer vision tasks used in advanced driver assistance systems (ADAS). Although numerous methods have been proposed for this task by leveraging deep convolutional neural networks (CNNs), stereo matching still remains an unsolved problem due to its inherent matching ambiguities. To overcome these limitations, we p...Show More

Adversarial Confidence Estimation Networks for Robust Stereo Matching

Sunok Kim;Dongbo Min;Seungryong Kim;Kwanghoon Sohn

IEEE Transactions on Intelligent Transportation Systems

Year: 2021 | Volume: 22, Issue: 11 | Journal Article |

An overview on sensor map based localization for automated driving

Liang Li;Ming Yang;Bing Wang;Chunxiang Wang

2017 Joint Urban Remote Sensing Event (JURSE)

Year: 2017 | Conference Paper |

Cited by: Papers (12)

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In the urban environment or under complex traffic situation, conventional localization methods like GPS, dead reckoning or SLAM are not precise enough for autonomous driving. So high-precision localization for intelligent vehicles becomes a hot problem. Among all the high-precision localization methods, the category based on sensor maps outperforms others because of its accuracy and real-time prop...Show More

An overview on sensor map based localization for automated driving

Liang Li;Ming Yang;Bing Wang;Chunxiang Wang

2017 Joint Urban Remote Sensing Event (JURSE)

Year: 2017 | Conference Paper |

Sparse representation to localize objects in underwater acoustic images

B. Akshaya;V. Narmadha;T.S. Sharmila;V. Rajendran

2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

Year: 2015 | Conference Paper |

Cited by: Papers (3)

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Screening of underwater images for the presence of submerged objects and phenomena like submarine volcanoes is based on the spotting and localization of objects present in underwater. This paper proposes a dictionary based approach in which a dictionary is created from various sub-images of the target image. The dictionary of sub-images is created for multiple scales. The coefficients are computed...Show More

Sparse representation to localize objects in underwater acoustic images

B. Akshaya;V. Narmadha;T.S. Sharmila;V. Rajendran

2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

Year: 2015 | Conference Paper |

Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network

Surya M. Ravishankar;Ryosuke Tsumura;John W. Hardin;Beatrice Hoffmann;Ziming Zhang;Haichong K. Zhang

2021 IEEE International Ultrasonics Symposium (IUS)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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Lung ultrasound (LUS) has been used for point-of-care diagnosis of respiratory diseases including COVID-19, with advantages such as low cost, safety, absence of radiation, and portability. The scanning procedure and assessment of LUS are highly operator-dependent, and the appearance of LUS images varies with the probe's position, orientation, and contact force. Karamalis et al. introduced the conc...Show More

Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network

Surya M. Ravishankar;Ryosuke Tsumura;John W. Hardin;Beatrice Hoffmann;Ziming Zhang;Haichong K. Zhang

2021 IEEE International Ultrasonics Symposium (IUS)

Year: 2021 | Conference Paper |

Probabilistic Method for Mapping & 3D SLAM of an off-road Terrain with Four Wheeled Robot

Kunal Bhujbal;Mahavir Devmane;Ananya More

2022 5th International Conference on Advances in Science and Technology (ICAST)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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Mobile robots use onboard range sensors and accurate, real-time mapping to perform autonomous navigation in challenging terrain. Absolute localisation based on the tracking of exterior geometric or visual cues is frequently used in existing techniques. To overcome the dependability issues with current methods, we propose a novel method for terrain mapping that only uses interoceptive clustering fr...Show More

Probabilistic Method for Mapping & 3D SLAM of an off-road Terrain with Four Wheeled Robot

Kunal Bhujbal;Mahavir Devmane;Ananya More

2022 5th International Conference on Advances in Science and Technology (ICAST)

Year: 2022 | Conference Paper |

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