Generalized Deformable Spatial Pyramid: Geometry-Preserving Dense Correspondence Estimation

Title
Generalized Deformable Spatial Pyramid: Geometry-Preserving Dense Correspondence Estimation
Authors
허준화임화섭박창수안상철
Issue Date
2015-06
Publisher
CVPR, IEEE conf. on Computer Vision and Pattern Recognition
Citation
VOL 1, NO 1, 1392-1400
Abstract
We present a Generalized Deformable Spatial Pyramid (GDSP) matching algorithm for calculating the dense correspondence between a pair of images with large appearance variations. The main challenges of the problem generally originate in appearance dissimilarities and geometric variations between images. To address these challenges, we improve the existing Deformable Spatial Pyramid (DSP) [10] model by generalizing the search space and devising the spatial smoothness. The former is leveraged by rotations and scales, and the latter simultaneously considers dependencies between high-dimensional labels through the pyramid structure. Our spatial regularization in the highdimensional space enables our model to effectively preserve the meaningful geometry of objects in the input images while allowing for a wide range of geometry variations such as perspective transform and non-rigid deformation. The experimental results on public datasets and challenging scenarios show that our method outperforms the stateof- the-art methods both qualitatively and quantitatively.
URI
http://pubs.kist.re.kr/handle/201004/50748
Appears in Collections:
KIST Publication > Conference Paper
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