no code implementations • 26 Feb 2022 • Zhe Ming Chng, Joseph Mun Hung Lew, Jimmy Addison Lee
Lane detection is an integral part of control systems in autonomous vehicles and lane departure warning systems as lanes are a key component of the operating environment for road vehicles.
1 code implementation • 19 Oct 2020 • Zhe Ming Chng, Joseph Mun Hung Lew, Jimmy Addison Lee
In this paper, we present a real-time robust neural network output enhancement for active lane detection (RONELD) method to identify, track, and optimize active lanes from deep learning probability map outputs.
no code implementations • ICCV 2019 • Jimmy Addison Lee, Peng Liu, Jun Cheng, Huazhu Fu
Spatial transformations are estimated based on the output possibility of the fully connected layer of CNN for a pair of images.
no code implementations • CVPR 2017 • Jacob Chan, Jimmy Addison Lee, Qian Kemao
This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description.
no code implementations • CVPR 2016 • Jacob Chan, Jimmy Addison Lee, Qian Kemao
This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition.
no code implementations • CVPR 2015 • Jimmy Addison Lee, Jun Cheng, Beng Hai Lee, Ee Ping Ong, Guozhen Xu, Damon Wing Kee Wong, Jiang Liu, Augustinus Laude, Tock Han Lim
These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration.