Dynamic Registration Correction in Video-Based Augmented Reality Systems
IEEE Computer Graphics and Applications
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Augmented Reality: Merging Real and Virtual Worlds
Spatial Augmented Reality: Merging Real and Virtual Worlds
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Radiometric compensation for a low-cost immersive projection system
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Undistorted projection onto dynamic surface
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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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.