ROBUST RECOVERY OF TRANSFORM INVARIANT LOW-RANK TEXTURES
First Claim
1. A method for extracting textural and geometric information from an image, comprising steps for:
- receiving an input image;
selecting an image channel from available channels of a color space of the input image;
windowing the input image;
for each window of the input image, iterating steps for;
normalizing the selected image channel,computing a Jacobian matrix from the normalized image channel with respect to a current geometric transformation,recovering a current low-rank texture from the normalized image channel by using an iterative convex optimization process to update the current geometric transformation and find the low-rank texture having a fewest number of nonzero entries that is equivalent to the normalized image channel relative to the current geometric transformation until local convergence of the geometric transformation is achieved, andrepeating the iterated steps for each window until global convergence of the geometric transformation is achieved; and
outputting the current low-rank texture and the current geometric transformation.
2 Assignments
0 Petitions
Accused Products
Abstract
A “Transform Invariant Low-Rank Texture” (TILT) Extractor, referred to as a “TILT Extractor” accurately extracts both textural and geometric information defining regions of low-rank planar patterns from 2D images of a scene, thereby enabling a large range of image processing applications. Unlike conventional feature extraction techniques that rely on point-based features, the TILT Extractor extracts texture regions from an image and derives global correlations or transformations of those regions in 3D (e.g., transformations including translation, rotation, reflection, skew, scale, etc.). These image domain transformations inherently provide information relative to an automatically determinable camera viewing direction. In other words, the TILT Extractor extracts low-rank regions and geometric correlations describing domain transforms of those regions relative to arbitrary camera viewpoints. The TILT Extractor also identifies sparse error in image intensity or other color channels resulting from noise, occlusions or other artifacts, thereby allowing elimination or reduction of such errors in images.
28 Citations
20 Claims
-
1. A method for extracting textural and geometric information from an image, comprising steps for:
-
receiving an input image; selecting an image channel from available channels of a color space of the input image; windowing the input image; for each window of the input image, iterating steps for; normalizing the selected image channel, computing a Jacobian matrix from the normalized image channel with respect to a current geometric transformation, recovering a current low-rank texture from the normalized image channel by using an iterative convex optimization process to update the current geometric transformation and find the low-rank texture having a fewest number of nonzero entries that is equivalent to the normalized image channel relative to the current geometric transformation until local convergence of the geometric transformation is achieved, and repeating the iterated steps for each window until global convergence of the geometric transformation is achieved; and outputting the current low-rank texture and the current geometric transformation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A system for deriving low-rank textures from an input image, comprising:
-
a device for receiving an input image; a device for windowing the input image; for each window of the input image, a device for iterating steps for; normalizing an intensity channel of the input image, computing a Jacobian matrix from the normalized intensity channel with respect to a current geometric transformation, recovering a current low-rank texture from the normalized intensity channel by using an iterative convex optimization process to update the current geometric transformation and a current sparse error component of the input image by finding the current low-rank texture having a fewest number of nonzero entries that is equivalent to the normalized intensity channel relative to the current geometric transformation and the current sparse error component of the input image until local convergence of the geometric transformation is achieved, and repeating the iterated steps for each window until global convergence of the geometric transformation is achieved; and a device for outputting the current low-rank texture, the current geometric transformation, and the current sparse error component. - View Dependent Claims (10, 11, 12, 13, 14)
-
-
15. A computer-readable storage device having computer executable instructions stored therein for extracting low-rank textural and geometric information and a sparse error component from an image, said instructions comprising:
-
a device for receiving an input image; a device for windowing the input image; for each window of the input image, a device for iterating steps for; normalizing an intensity channel of the input image, computing a Jacobian matrix from the normalized intensity channel with respect to a current geometric transformation, recovering a current low-rank texture from the normalized intensity channel by using an iterative convex optimization process to update the current geometric transformation and a current sparse error component of the input image by iteratively solving; - View Dependent Claims (16, 17, 18, 19, 20)
-
Specification