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Content adaptive image matching by color-entropy segmentation and inpainting
Conference
Sun, Y, Xin, J. (2011). Content adaptive image matching by color-entropy segmentation and inpainting .
EURO-PAR 2011 PARALLEL PROCESSING, PT 1,
6855 LNCS(PART 2), 471-478. 10.1007/978-3-642-23678-5_56
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Sun, Y, Xin, J. (2011). Content adaptive image matching by color-entropy segmentation and inpainting .
EURO-PAR 2011 PARALLEL PROCESSING, PT 1,
6855 LNCS(PART 2), 471-478. 10.1007/978-3-642-23678-5_56
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cited authors
Sun, Y; Xin, J
authors
Sun, Yuanchang
abstract
Image matching is a fundamental problem in computer vision. One of the well-known techniques is SIFT (scale-invariant feature transform). SIFT searches for and extracts robust features in hierarchical image scale spaces for object identification. However it often lacks efficiency as it identifies many insignificant features such as tree leaves and grass tips in a natural building image. We introduce a content adaptive image matching approach by preprocessing the image with a color-entropy based segmentation and harmonic inpainting. Natural structures such as tree leaves have both high entropy and distinguished color, and so such a combined measure can be both discriminative and robust. The harmonic inpainting smoothly interpolates the image functions over the tree regions and so blurrs and reduces the features and their unnecessary matching there. Numerical experiments on building images show around 10% improvement of the SIFT matching rate with 20% to 30% saving of the total CPU time. © 2011 Springer-Verlag.
publication date
September 20, 2011
published in
DISTRIBUTED COMPUTING (DISC 2014)
Book
Identifiers
Digital Object Identifier (DOI)
https://doi.org/10.1007/978-3-642-23678-5_56
Additional Document Info
start page
471
end page
478
volume
6855 LNCS
issue
PART 2