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Automatic extraction of local features from images plays an important role in many computer vision tasks. During the last years, much focus has been put on making the features invariant to geometric transformations such as a rotation and scaling of the image. Recently, some work has been published concerning the integration of color information into the detection and description step of SIFT. In various evaluations, it has been shown that including color information can increase distinctiveness and invariance to photometric transformations caused by illumination changes. In this paper we build on the results from these approaches and apply them to the SURF descriptor, which is advantageous compared to SIFT in terms of speed, making it a perfect candidate for online applications, for example in the field of robotics. Our results show significant improvements concerning the repeatability and destinctiveness of SURF for 3D objects under varying illumination directions.
In contrast to many other evaluations we also determine the accuracy of the orientation assignment and include this into our comparisons.
