Title :
Shape Matching Using Multiscale Integral Invariants
Author :
Byung-Woo Hong ; Soatto, Stefano
Author_Institution :
Comput. Sci. Dept., Chung-Ang Univ., Seoul, South Korea
Abstract :
We present a shape descriptor based on integral kernels. Shape is represented in an implicit form and it is characterized by a series of isotropic kernels that provide desirable invariance properties. The shape features are characterized at multiple scales which form a signature that is a compact description of shape over a range of scales. The shape signature is designed to be invariant with respect to group transformations which include translation, rotation, scaling, and reflection. In addition, the integral kernels that characterize local shape geometry enable the shape signature to be robust with respect to undesirable perturbations while retaining discriminative power. Use of our shape signature is demonstrated for shape matching based on a number of synthetic and real examples.
Keywords :
computational geometry; computer vision; feature extraction; image matching; image representation; shape recognition; Wasserstein distance; computer vision; image processing; integral kernels; local shape geometry characterization; multiscale integral invariants; reflection transformation; rotation transformation; scaling transformation; shape analysis; shape descriptor; shape feature characterization; shape matching; shape representation; shape signature; translation transformation; Indexes; Kernel; Noise; Pattern recognition; Robustness; Shape; Shape measurement; Shape matching; Wasserstein distance; integral invariant; scale invariant; shape descriptor;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2014.2342215