Direct computation of shape cues using scale-adapted spatial derivative operators
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry-based 3-D reconstruction from perspective images
Computer Vision and Image Understanding
Extraction, matching, and pose recovery based on dominant rectangular structures
Computer Vision and Image Understanding
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
TapTell: understanding visual intents on-the-go
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Unsupervised upright orientation of man-made models
Graphical Models
Repetition Maximization based Texture Rectification
Computer Graphics Forum
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Graph-Based detection of objects with regular regions
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Efficient clothing retrieval with semantic-preserving visual phrases
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Shadow-Free TILT for facade rectification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Quasi-regular facade structure extraction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Transform invariant text extraction
The Visual Computer: International Journal of Computer Graphics
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In this paper, we show how to efficiently and effectively extract a rich class of low-rank textures in a 3D scene from 2D images despite significant distortion and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as all kinds of regular, symmetric patterns ubiquitous in urban environments and manmade objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant projective deformation, our method can accurately recover both the intrinsic low-rank texture and the precise domain transformation. Extensive experimental results demonstrate that this new technique works effectively for many nearregular patterns or objects that are approximately low-rank, such as human faces and text.