DocumentCode
2712377
Title
Discrete texture traces: Topological representation of geometric context
Author
Ernst, Jan ; Singh, Maneesh K. ; Ramesh, Visvanathan
Author_Institution
Corp. Res. & Technol., Siemens Corp., Princeton, NJ, USA
fYear
2012
fDate
16-21 June 2012
Firstpage
422
Lastpage
429
Abstract
Modeling representations of image patches that are quasi-invariant to spatial deformations is an important problem in computer vision. In this paper, we propose a novel concept, the texture trace, that allows sparse patch representations which are quasi-invariant to smooth deformations and robust against occlusions. We first propose a continuous domain model, the profile trace, which is a function only of the topological properties of an image and is by construction invariant to any homeomorphic transformation of the domain. We analyze its theoretical properties and then derive a discrete-domain approximation, the Discrete Texture Trace (DTT). DTTs are designed to be computationally practical and shown by a set of controlled experiments to be quasi-invariant to smooth spatial deformations as well as common image perturbations. We then show how DTTs can be naturally adapted to the incremental tracking problem, yielding highly precise results on par with the state of the art on challenging real data without using heavy machine learning tools. Indeed, we show that with even just using one image at the start of a sequence (i.e. no incremental updating), our method already outperforms four of six state of the art methods of the recent literature on challenging sequences.
Keywords
computer vision; geometry; image representation; image sequences; image texture; object tracking; topology; DTT; computer vision; continuous domain model; discrete texture trace; discrete-domain approximation; geometric context; homeomorphic transformation; image patch modeling representation; image perturbation; image sequence; incremental tracking problem; profile trace; quasiinvariant image; smooth spatial deformation; sparse patch representation; topological properties; topological representation; Approximation methods; Computational modeling; Deformable models; Image edge detection; Mathematical model; Noise; Tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4673-1226-4
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2012.6247704
Filename
6247704
Link To Document