Search Results for author: Jimmy Addison Lee

Found 6 papers, 1 papers with code

RONELDv2: A faster, improved lane tracking method

no code implementations26 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.

Lane Detection

RONELD: Robust Neural Network Output Enhancement for Active Lane Detection

1 code implementation19 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.

Lane Detection

A Deep Step Pattern Representation for Multimodal Retinal Image Registration

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.

Image Registration

BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition

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.

Object Object Recognition

BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition

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.

Line Segment Detection Object +1

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