G
Gu Fang
Researcher at University of Sydney
Publications - 104
Citations - 2202
Gu Fang is an academic researcher from University of Sydney. The author has contributed to research in topics: Robot welding & Welding. The author has an hindex of 22, co-authored 104 publications receiving 1921 citations. Previous affiliations of Gu Fang include University of Western Sydney & University of Technology, Sydney.
Papers
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Journal ArticleDOI
Human Object Recognition Using Colour and Depth Information from an RGB-D Kinect Sensor
Benjamin J. Southwell,Gu Fang +1 more
TL;DR: Experiments show that the proposed shirt segmentation and colour recognition method can recognize shirts of varying colours and patterns robustly and is light invariant compared to colour-based segmentation methods.
Proceedings ArticleDOI
Evolutionary particle filter: re-sampling from the genetic algorithm perspective
TL;DR: The contribution of this paper is in the proposal of a hybrid technique to mitigate sample impoverishment such that the number of particles required and hence the computation complexities are reduced.
Journal ArticleDOI
Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding
Mitchell Dinham,Gu Fang +1 more
TL;DR: In this paper, a method for the automatic identification and location of welding seams for robotic welding using computer vision is presented, which can provide a 3D Cartesian accuracy of within ± 1mm which is acceptable in most robotic arc welding applications.
Journal ArticleDOI
Computer vision technology for seam tracking in robotic GTAW and GMAW
TL;DR: A set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images and secondly, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters.
Journal ArticleDOI
Application of artificial neural networks in regional flood frequency analysis: a case study for Australia
TL;DR: Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT.