Oral Session 3: Machine Learning and Human Computer Interaction

Wednesday 15:30-17:00

15:30 O3.1 Classification of White Blood Cells Using Bispectral Invariant Features of Nuclei Shape
Khamael AL-DULAIMI, Queensland University of Technology, Australia;
Vinod Chandran, Queensland University of Technology, Australia;
Jasmine Banks, Queensland University of Technology, Australia;
Inmaculada Tomeo-Reyes, University of New South Wales, Australia;
Kien Nguyen, Queensland University of Technology, Australia

15:45 O3.2 Improving Supervised Microaneurysm Segmentation using Autoencoder-Regularized Neural Network
Rangwan Kasantikul, Faculty of Information and Communication Technology, Mahidol University, Thailand;
Worapan Kusakunniran, Faculty of Information and Communication Technology, Mahidol University, Thailand

16:00 O3.3 Adversarial Context Aggregation Network for Low-Light Image Enhancement
Yong-Goo Shin, Korea University, South Korea;
Min-Cheol Sagong, Korea University, South Korea;
Yoon-Jae Yeo, Korea University, South Korea;
Sung-Jea Ko, Korea University, South Korea

16:15 O3.4 Image Restoration based on Deep Convolutional Network in Wavefront Coding Imaging System
Haoyuan Du, Beijing institute of technology, China;
Liquan Dong, Beijing institute of technology, China;
Ming Liu, Beijing institute of technology, China;
Yuejin Zhao, Beijing institute of technology, China;
Wei Jia, Beijing institute of technology, China;
Xiaohua Liu, Beijing institute of technology, China;
Mei Hui, Beijing institute of technology, China;
Lingqin Kong, Beijing institute of technology, China;
Qun Hao, Beijing institute of technology, China

16:30 O3.5 Bi-Modal Content Based Image Retrieval using Multi-class Cycle-GAN
Girraj Pahariya, IIT Madras, India

16:45 O3.6 Multi-Class Recognition using Noisy Training Data with a Self-Learning Approach
Amir Ghahremani, Eindhoven university of technology, Netherlands;
Egor Bondarev, Eindhoven university of technology, Netherlands;
Peter H.N. de With, Eindhoven university of technology, Netherlands