Oral Session 2: Computer Vision

Tuesday 15:30-17:00

15:30 O2.1 Railway Infrastructure Defects Recognition Using Fine-grained Deep Convolutional Neural Networks
Huaxi Huang, University of Technology Sydney, Australia;
Jingsong Xu, University of Technology Sydney, Australia;
Jian Zhang, University of Technology Sydney, Australia;
Qiang Wu, University of Technology Sydney, Australia;
Christina Kirsch, Sydney Trains, Australia

15:45 O2.2 Colour Analysis of Strawberries on a Real Time Production Line
Gilbert Eaton, Griffith University, Australia;
Yongsheng Gao, Griffith University, Australia;
Andrew Busch, Griffith University, Australia;
Rudi Bartels, Griffith University, Australia

16:00 O2.3 Deep Bi-Dense Networks for Image Super-Resolution
Yucheng Wang, Baidu, China;
Jialiang Shen, University of Technology Sydney, Australia;
Jian Zhang, University of Technology Sydney, Australia

16:15 O2.4 Memory optimized Deep Dense Network for Image Super-resolution
Jialiang Shen, University of Technology Sydney, Australia;
Yucheng Wang, Baidu, China;
Jian Zhang, University of Technology Sydney, Australia

16:30 O2.5 In Situ Cane Toad Recognition
Dmitry Konovalov, James Cook University, Australia;
Simindokht Jahangard, Isfahan University of Medical Science, Iran, Islamic Republic of;
Lin Schwarzkopf, James Cook University, Australia

16:45 O2.6 Absolute and Relative Pose Estimation of a Multi-View Camera System using 2D-3D Line Pairs and Vertical Direction
Hichem Abdellali, University of Szeged, Szeged, Hungary;
Zoltan Kato, University of Szeged, Szeged, Hungary