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Finding reliable correspondence in two or more images remains a difficult and critical step in many computer vision tasks. The performance of descriptors determines the matching results directly. Compared with other descriptors, the Scale Invariant Feature Transform (SIFT) has been used widely for its superiority in invariant attributes, while it will fail in the case of locally visual aliasing. To...
This paper presents a novel approach for matching 2D points between a video projector and a digital camera. Our method is motivated by camera-projector applications for which the projected image needs to be warped to prevent geometric distortion. Since the warping process often needs geometric information on the 3D scene that can only be obtained from triangulation, we propose a technique for matching...
SIFT (scale invariant feature transform) is an important local invariant feature descriptor. Since its expensive computation, SURF (speeded-up robust features) is proposed. Both of them are designed mainly for gray images. However, color provides valuable information in object description and matching tasks. To overcome the drawback and to increase the descriptor's distinctiveness, this paper presents...
This paper presents an evaluation of the SIFT (scale invariant feature transform), Colour SIFT, and SURF (speeded up robust feature) descriptors on very low resolution images. The performance of the three descriptors are compared against each other on the precision and recall measures using ground truth correct matching data. Our experimental results show that both SIFT and colour SIFT are more robust...
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