<title>Finding colored objects under different illumination conditions in robotic applications</title>

Abstract

Color information is very useful for object recognition, but the measured image color of objects depends on the scene illumination. Human vision exhibits color constancy for an object under a wide range of illumination conditions. A similar ability is required if computer vision systems are used to recognize objects in uncontrolled environments. Until now there is no satisfying approach for finding a colored object under different illuminations and using different cameras. To find the colored object, the following approach is proposed and has been verified with several demonstrations. The first step is to take a picture of the known object and a reference template of eighteen colored strips. Using the color values of the reference template the object color can be related to the reference colors. The key idea is that the robot can take the template with the 18 colored strips with it. In other rooms with other illumination conditions the robot can look at this reference template and calibrate its camera to different illumination conditions. The color calibration of the camera is achieved with a transformation matrix and the standard deviation of the color transformation is used to evaluate the quality of the transformation. Under the new illumination conditions, this transformation is used to search for the color of the object in a new room. The found object color is related to the reference values of the sample using the transformation into the new color space. The colored object itself is found using the multi-spectral classification via Gaussian distribution. First demonstrations indicate that this approach is very robust against different illumination conditions ranging form neon light to direct sun light and can be used rather simple by each robot.