Illumination Independent Object Recognition

Lovell, Nathan

doi:10.1007/11780519_34

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Several papers investigate the benefits of first doing a segmentation or edgedectection step, and then classify the colors of whole segments. [15] and [16] use such methods with the main aims of improved color recognition and fast processing time. In [17] it was found that among different alternatives the method of choosing an unsure color ("'maybe-color"') to fit to its surrounding ones gave the best results.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Recognition of Color-Coded Objects in Indoor and Outdoor Environments

Takahashi

Nowak

Wisspeintner

2008

RoboCup 2007: Robot Soccer World Cup XI

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To achieve robust color perception under varying light conditions in indoor and outdoor environments, we propose a three-step method consisting of adaptive camera parameter control, image segmentation and color classification. A controller for the intrinsic camera parameters is used to improve color stability in the YUV space. Segmentation is done to detect spatially coherent regions of uniform color belonging to objects in the image. Then, a probabilistic classification method is applied to label the colors by use of a Gaussian color distribution model. Experiments under combination of artificial and natural illuminations indoors and outdoors have been carried out. The results show the feasibility of this approach as well as the problems that occur under these highly diverse light situations. In particular we investigate the application in a RoboCup soccer scenario pointing toward future outdoor use.

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Section: Related Workmentioning

confidence: 99%

Adaptive Recognition of Color-Coded Objects in Indoor and Outdoor Environments

Takahashi

Nowak

Wisspeintner

2008

RoboCup 2007: Robot Soccer World Cup XI

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“…Most of the teams recognize and locate objects from a rough segmentation (e.g. [1]), applying more sophisticated recognition techniques (e.g., region growing) at a later stage. However, this second approach may be less reliable or require more computational resources.…”

Section: Introductionmentioning

confidence: 99%

Robust Color Classification Using Fuzzy Reasoning and Genetic Algorithms in RoboCup Soccer Leagues

Kashani-Pour

Milani

Akhlaghi

et al. 2008

RoboCup 2007: Robot Soccer World Cup XI

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Color segmentation is typically the first step of vision processing for a robot operating in a color coded environment, like RoboCup soccer, and many object recognition modules rely on that, in this paper we present a method for color segmentation that is based on fuzzy logic. Fuzzy sets are defined on the H, S and L components of the HSL color space and provide a fuzzy logic model that aims to follow the human intuition of color classification. The membership functions used for the fuzzy inference are optimized by genetic algorithms. The method requires the setting of only a few parameters and has been proved to be very robust to noise and light variations, allowing for setting parameters only once. The approach has been implemented on MRL middle size robots, and successfully experimented in the numbers of the friendly matches of the Middle size in the 2006's games.

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“…Since good color segmentation allows for easy implementation of object recognition and localization, most of the robot vision systems are based on fast and accurate implementation of such process. Conversely, it is also possible to recognize and locate objects from a rough segmentation (e.g., [3]), applying more sophisticated recognition techniques (e.g., region growing) at a later stage. However, this second approach may be less reliable or require more computational resources.…”

Section: Introductionmentioning

confidence: 99%

Robust Color Segmentation Through Adaptive Color Distribution Transformation

Iocchi

2007

RoboCup 2006: Robot Soccer World Cup X

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Color segmentation is typically the first step of vision processing for a robot operating in a color-coded environment, such as RoboCup soccer, and many object recognition modules rely on that.Although many approaches to color segmentation have been proposed, in the official games of the RoboCup Four Legged League manual calibration is still preferred by most of the teams. In this paper we present a method for color segmentation that is based on an adaptive transformation of the color distribution of the image: the transformation is dynamically computed depending on the current image (i.e., it adapts to condition changes) and then it is used for color segmentation with static thresholds. The method requires the setting of only a few parameters and has been proved to be very robust to noise and light variations, allowing for setting parameters only once when arriving at a competition site.The approach has been implemented on AIBO robots, extensively tested in our laboratory, and successfully experimented in the some of the games of the Four Legged League in RoboCup 2005.

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Illumination Independent Object Recognition

Cited by 7 publications

References 23 publications

Adaptive Recognition of Color-Coded Objects in Indoor and Outdoor Environments

Adaptive Recognition of Color-Coded Objects in Indoor and Outdoor Environments

Robust Color Classification Using Fuzzy Reasoning and Genetic Algorithms in RoboCup Soccer Leagues

Robust Color Segmentation Through Adaptive Color Distribution Transformation

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