TILT: Transform Invariant Low-Rank Textures
نویسندگان
چکیده
منابع مشابه
Low Complexity Low Rank Transform Domain Adaptive Filtering
This paper introduces an efficient algorithm to solve the low rank transform domain adaptive filtering problem within the framework introduced in [9]. The method in [9] extracts an underdetermined solution from an overdetermined least squares problem, using a unitary transformation. The optimal transforms as derived in [9] dramatically improved performance, at the expense of increased complexit...
متن کاملTextures on Rank-1 Lattices
Storing textures on orthogonal tensor product lattices is predominant in computer graphics, although it is known that their sampling efficiency is not optimal. In two dimensions, the hexagonal lattice provides the maximum sampling efficiency. However, handling these lattices is difficult, because they are not able to tile an arbitrary rectangular region and have an irrational basis. By storing ...
متن کاملA low rank based seismic data interpolation via frequency-patches transform and low rank space projection
We propose a new algorithm to improve computational efficiency for low rank based interpolation. The interpolation is carried out in the frequency spatial domain where each frequency slice is first transferred to the frequency-patches domain. A nice feature of this domain is that the number of non-zero singular values can be better related to seismic events, which favors low rank reduction. Dur...
متن کاملAffine-Invariant Online Optimization and the Low-rank Experts Problem
We present a new affine-invariant optimization algorithm calledOnline Lazy Newton. The regret of Online Lazy Newton is independent of conditioning: the algorithm’s performance depends on the best possible preconditioning of the problem in retrospect and on its intrinsic dimensionality. As an application, we show how Online Lazy Newton can be used to achieve an optimal regret of order √ rT for t...
متن کاملDomain-invariant Face Recognition using Learned Low-rank Transformation
We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination. The key idea is to learn discriminative linear transformations for face images using matrix rank as the optimization criteria. The learned linear transformations restore a shared low-rank structure for faces from the same subject, and, at the same time...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2012
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-012-0515-x