TILT: Transform Invariant Low-rank Textures

Abstract. 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 an...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Zhengdong Zhang, Arvind Ganesh, Xiao Liang, Yi Ma
• Keywords: CORR 2010, Education, Gross Sparse Errors, Intrinsic Low-rank Texture, Low-rank Texture