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Robust vision system to illumination changes in a color-dependent task

机译:强大的视觉系统可根据颜色改变任务中的照明变化

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摘要

Most computer vision tasks are strongly sensitive to illumination changes. This is the case of RoboCup competitions, being color dependent tasks, they require a robust color-based segmentation method of the image for object recognition. It is difficult to achieve a constant illumination in a work environment that is subject to day light changes. This is why camera parameters calibration such as exposure time must be robust enough to reduce the impact in the output images. A result comparison on the segmentation of images is made in three different color spaces, RGB, HSV, and YCrCb. We researched their connection with changes in lighting condition and select one of them that have less error for those environments. We introduce a fuzzy calibration method of the camera exposure time parameter, by histograms correlation. The calibrations results give an approximate value of the correct parameter to be modified in the camera in order to avoid affecting the thresholds of segmentation in the color recognition task.
机译:大多数计算机视觉任务对照明变化非常敏感。 RoboCup竞赛就是这种情况,它是颜色相关的任务,它们需要基于鲁棒的基于颜色的图像分割方法来进行对象识别。在日光变化的工作环境中,很难获得恒定的照明。这就是为什么相机参数校准(例如曝光时间)必须足够坚固以减少对输出图像的影响的原因。在三个不同的颜色空间RGB,HSV和YCrCb中对图像分割进行了结果比较。我们研究了它们与照明条件变化的关系,并选择其中一种对那些环境的误差较小的方法。我们通过直方图相关性介绍了相机曝光时间参数的模糊校正方法。校准结果给出了要在相机中修改的正确参数的近似值,以避免影响颜色识别任务中的分割阈值。

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