Aleksandr Setkov
ABSTRACT
The success of matching algorithms relies on the definition of features which are both invariant against the geometric distortions to be considered, and distinctive enough to avoid ambiguities. This paper addresses the problem of color feature points matching under photometric and geometric changes. Considering the popular SURF descriptor, it analyzes its state-of-the-art color versions, and proposes a new extension by using local histogram equalization (LHE). While most existing descriptors stem from color conversions and apply to standard lighting variations acquired by the same device, the proposed feature is device-independent and could fit to very generic changes.
The experimental results show that the proposed color descriptors outperform the existing ones under some types of distortions, and are more precise and invariant to different color variations. The paper considers Projector-based Augmented Reality (PAR) as an application field, where one of the evaluation criteria is homography accuracy between real and estimated distorted images. The results show that the proposed method gives the most stable results over all the other techniques and therefore they justify its use for robust color feature matching and its application to geometric correction.
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Index Terms
- Color invariant feature matching for image geometric correction
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