Techniques for Obtaining Robust, Real-Time, Colour-Based Vision for Robotics

Authors:
James Brusey;Lin Padgham
Affiliations:
-;-
Venue:
RoboCup-99: Robot Soccer World Cup III
Year:
2000

Citing 9
Cited 8

Graphics gems

Graphics gems
Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
C4.5: programs for machine learning

C4.5: programs for machine learning
Real-time colour recognition in symbolic programming for machine vision systems

Machine Vision and Applications
Real-Time Color Detection System Using Custom LSI for High-Speed Machine Vision

RoboCup-99: Robot Soccer World Cup III
Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario

RoboCup-99: Robot Soccer World Cup III
A Segmentation System for Soccer Robot Based on Neural Networks

RoboCup-99: Robot Soccer World Cup III
A Method for Localization by Integration of Imprecise Vision and a Field Model

RoboCup-99: Robot Soccer World Cup III
Image Processing, Analysis, and Machine Vision

Image Processing, Analysis, and Machine Vision

Implementing Computer Vision Algorithms in Hardware: An FPGA/VHDL-Based Vision System for a Mobile Robot

RoboCup 2001: Robot Soccer World Cup V
A Segmentation System for Soccer Robot Based on Neural Networks

RoboCup-99: Robot Soccer World Cup III
RMIT Raiders

RoboCup-99: Robot Soccer World Cup III
A Color Segmentation Algorithm for Real-Time Object Localization on Small Embedded Systems

RobVis '01 Proceedings of the International Workshop on Robot Vision
Hand tracking for low powered mobile AR user interfaces

AUIC '05 Proceedings of the Sixth Australasian conference on User interface - Volume 40
Robust method of detecting moving objects in videos evolved by genetic programming

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Plug and play: fast automatic geometry and color calibration for cameras tracking robots

RoboCup 2004
Real-Time adaptive colour segmentation for the robocup middle size league

RoboCup 2004

Quantified Score

Hi-index	0.00

Visualization

Abstract

An early stage in image understanding using colour involves recognizing the colour of target objects by looking at individual pixels. However, even when, to the human eye, the colours in the image are distinct, it is a challenge for machine vision to reliably recognize the whole object from colour alone, due to variations in lighting and other environmental issues. In this paper, we investigate the use of decision trees as a basis for recognizing colour. We also investigate the use of colour space transforms as a way of eliminating variations due to lighting.