Automatic On-Line Color Calibration Using Class-Relative Color Spaces

Guerrero, Pablo; Ruiz‐del‐Solar, Javier; Fredes, Josué; Palma-Amestoy, Rodrigo

doi:10.1007/978-3-540-68847-1_22

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Heinemann et al [7] in 2007 proposed a colour training algorithm for the RoboCup [13] four-legged (AIBO) league that matches an acquired colour information with prior knowledge of the geometry of the playing field. More recently, knowledge of the spatial relationship between colour classes in a colour space was utilised to develop an adaptive colour classification technique that works even when there are sudden illumination changes [8]. However, spatial illumination variations in the field and discrimination between similar colours were not taken into account.…”

Section: Related Workmentioning

confidence: 99%

“…However, the colour clusterings are unstable even when the object being tracked down is under a fixed illumination because quantum electrical effects in the camera sensor chip easily distorts the colours captured and cannot be prevented. Even worse, the colour clusters drift in the colour space due to spatially varying illuminations [2,[5][6][7][8][9][10]. Figure 1 illustrates a snapshot of solid colour patches under spatially varying illuminations.…”

Section: Problem Domainmentioning

confidence: 99%

“…The colour classification process we refer to here pertains to correctly distinguishing colours belonging to the same object as it moves across the scene, with the presence of other similar colours. Although there are many researches about colour classification, only few researches successfully classify colours under varying illuminations [2,[5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Shin

Reyes

2009

Memetic Comp.

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This paper presents a complete Fuzzy-Geneticbased self-calibrating illumination intensity-invariant colour classification system. Previously, we have developed a novel fuzzy colour processing technique called Fuzzy Colour Contrast Fusion (FCCF) that selectively and adaptively corrects colours depicting target colour objects. FCCF has been proven to compensate for the effects of spatially varying illumination intensities in the scene, in various colour spaces. However, FCCF requires a huge set of parameters that is extremely tedious to calibrate by hand. To address these problems, we present a system that combines FCCF with a Heuristic-Assisted Genetic Algorithm (HAGA). FCCF-HAGA fully automates the fine-tuning of all colour descriptors, with significantly improved colour classification accuracy. Furthermore, we have reduced FCCF's storage space requirements by processing colour channels selectively at varying colour depths. This is accomplished by combining a Variable Colour Depth (VCD) algorithm with FCCF that searches for the most effective colour depth for each colour channel. Our results show that for all cases, the FCCF-HAGA-VCD combination improves pie-slice colour classification. For six different target colours, under varying illuminations, the hybrid algorithm was able to yield 17.63% higher overall colour classification accuracy as compared to the pure fuzzy approach. Furthermore, it was able to reduce LUT storage space requirements by 78.06%, as compared to the full-colour depth LUT.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding near optimum colour classifiers: genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth

Shin

Reyes

2009

Memetic Comp.

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“…Where there are one or two dominant colour classes that can be detected unambiguously, these can be used as a reference for mapping the remaining detected colours to known classes [19]. In our experiments on an FPGA, we want to operate on streamed images to limit the resources used by our design.…”

Section: Adaptive Approachesmentioning

confidence: 99%

Towards automatic colour segmentation for robot soccer

Bailey

Contreras

Gupta

2015

2015 6th International Conference on Automation, Robotics and Applications (ICARA)

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Tuning the colour thresholds within robot soccer is laborious, and sensitive to changes in illumination. An algorithm for automatic gain control and white balancing within the camera is described. This significantly reduces the effects of the variations in lighting on the thresholds. Next, a new colour space is proposed which maximises the hue separation of the different coloured regions to improve the colour discrimination. Finally several automatic segmentation techniques are briefly discussed. All of the algorithms are designed to operate on data directly streamed from the camera, enabling a low latency FPGA implementation.

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