Robust Color Segmentation Through Adaptive Color Distribution Transformation

Iocchi, Luca

doi:10.1007/978-3-540-74024-7_25

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Although some tracking and localization algorithms used in the RoboCup Soccer Leagues still relays on colored artificial landmarks (Iocchi, 2007) there is already research in the Four Legged League for self localization of the robots based on field features and not in colors (Herrero-Pérez, 2007). These algorithms can be use in any kind of unmanned vehicles to detect features in the terrain around it and self locating its position.…”

Section: Image Processing Self-localization Sensor Fusionmentioning

confidence: 99%

Robotic Soccer: The Gateway for Powerful Robotic Applications

Celiberto¹,

Matsuura²

2008

Proceedings of the Fifth International Conference on Informatics in Control Automation and Robotics Service

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From the RoboCup goal of having a fully autonomous humanoid soccer team, it is possible to see many applications of the research in the Humanoid Soccer, such as the development of mechanical legs and arms and exoskeletons. The onboard vision algorithms for multi target tracking and the cooperative decision making of some Soccer Leagues can be used in squadrons of autonomous vehicles in a variety of missions. The algorithms for image processing of the Small Size League can be used in aerial or satellite images to track vehicles. The Simulation League allows the development of many intelligent agents applications. The formations and team play positioning of the Simulation League can be used to optimize the positioning of a squadron of autonomous vehicles. The research of Robotic Soccer fosters and strengthens the research in Robotics, allowing and contributing to the development of many powerful applications which can great benefit the mankind.

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Section: Image Processing Self-localization Sensor Fusionmentioning

confidence: 99%

Robotic Soccer: The Gateway for Powerful Robotic Applications

Celiberto¹,

Matsuura²

2008

Proceedings of the Fifth International Conference on Informatics in Control Automation and Robotics Service

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“…2 Background and Related Work SPQR Team contributed with the development of a dynamic color segmentation method [2] that is able to provide robust and efficient color segmentation with very little calibration effort, as well as a hierarchical approach for examining the image pixels using a set of sentinel pixels that are non-uniformly spread on the image.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for RealisticBall Detection in RoboCup SPL

Bloisi,

Del Duchetto,

Manoni

et al. 2017

Preprint

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In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The approach has been tested in multiple environments, both indoor and outdoor. Furthermore, the ball detector described in this technical report has been used by the SPQR Robot Soccer Team during the competitions of the Robocup German Open 2017. To facilitate the use of our code by other teams, we have prepared a step-by-step installation guide.

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“…These included an orange ball and blue and yellow coloured goal posts. Consequently, most teams at RoboCup investigate methods to implement stable and efficient colour vision, including colour segmentation in the presence of illumination changes on their robots [4,[6][7][8]. This is in contrast to general mobile robots that avoid colour perception issues by using laser range finders and sonar not unlike those used for the DARPA challenges.…”

Section: Introductionmentioning

confidence: 99%

A Fast Method for Adapting Lookup Tables Applied to Changes in Lighting Colour

Houliston

Metcalfe

Chalup

2015

RoboCup 2015: Robot World Cup XIX

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This paper proposes a simple and fast method for adapting colour lookup tables to lighting changes in real-time. The method adjusts the classified colour space regions keeping both their surface area and volume constant. Two variations of the method were compared and tested in a RoboCup soccer setting. Detection success rate was measured as a function of the speed and magnitude of hue change to the lighting environment. Compared to a static lookup table, these experimental results show improved robustness against lighting changes for detection of coloured objects.

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Robust Color Segmentation Through Adaptive Color Distribution Transformation

Cited by 10 publications

References 5 publications

Robotic Soccer: The Gateway for Powerful Robotic Applications

Robotic Soccer: The Gateway for Powerful Robotic Applications

Machine Learning for RealisticBall Detection in RoboCup SPL

A Fast Method for Adapting Lookup Tables Applied to Changes in Lighting Colour

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