3 research outputs found
Subspace Learning in The Presence of Sparse Structured Outliers and Noise
Subspace learning is an important problem, which has many applications in
image and video processing. It can be used to find a low-dimensional
representation of signals and images. But in many applications, the desired
signal is heavily distorted by outliers and noise, which negatively affect the
learned subspace. In this work, we present a novel algorithm for learning a
subspace for signal representation, in the presence of structured outliers and
noise. The proposed algorithm tries to jointly detect the outliers and learn
the subspace for images. We present an alternating optimization algorithm for
solving this problem, which iterates between learning the subspace and finding
the outliers. This algorithm has been trained on a large number of image
patches, and the learned subspace is used for image segmentation, and is shown
to achieve better segmentation results than prior methods, including least
absolute deviation fitting, k-means clustering based segmentation in DjVu, and
shape primitive extraction and coding algorithm.Comment: IEEE International Symposium on Circuits and Systems, 201
Text Extraction From Texture Images Using Masked Signal Decomposition
Text extraction is an important problem in image processing with applications
from optical character recognition to autonomous driving. Most of the
traditional text segmentation algorithms consider separating text from a simple
background (which usually has a different color from texts). In this work we
consider separating texts from a textured background, that has similar color to
texts. We look at this problem from a signal decomposition perspective, and
consider a more realistic scenario where signal components are overlaid on top
of each other (instead of adding together). When the signals are overlaid, to
separate signal components, we need to find a binary mask which shows the
support of each component. Because directly solving the binary mask is
intractable, we relax this problem to the approximated continuous problem, and
solve it by alternating optimization method. We show that the proposed
algorithm achieves significantly better results than other recent works on
several challenging images.Comment: arXiv admin note: text overlap with arXiv:1704.0771
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
This work presents a new robust PCA method for foreground-background
separation on freely moving camera video with possible dense and sparse
corruptions. Our proposed method registers the frames of the corrupted video
and then encodes the varying perspective arising from camera motion as missing
data in a global model. This formulation allows our algorithm to produce a
panoramic background component that automatically stitches together corrupted
data from partially overlapping frames to reconstruct the full field of view.
We model the registered video as the sum of a low-rank component that captures
the background, a smooth component that captures the dynamic foreground of the
scene, and a sparse component that isolates possible outliers and other sparse
corruptions in the video. The low-rank portion of our model is based on a
recent low-rank matrix estimator (OptShrink) that has been shown to yield
superior low-rank subspace estimates in practice. To estimate the smooth
foreground component of our model, we use a weighted total variation framework
that enables our method to reliably decouple the true foreground of the video
from sparse corruptions. We perform extensive numerical experiments on both
static and moving camera video subject to a variety of dense and sparse
corruptions. Our experiments demonstrate the state-of-the-art performance of
our proposed method compared to existing methods both in terms of foreground
and background estimation accuracy.Comment: IEEE TCI. Project webpage: https://gaochen315.github.io/pRPCA/ Code:
https://github.com/gaochen315/panoramicRPC