1,069 research outputs found
On the Subspace of Image Gradient Orientations
We introduce the notion of Principal Component Analysis (PCA) of image
gradient orientations. As image data is typically noisy, but noise is
substantially different from Gaussian, traditional PCA of pixel intensities
very often fails to estimate reliably the low-dimensional subspace of a given
data population. We show that replacing intensities with gradient orientations
and the norm with a cosine-based distance measure offers, to some
extend, a remedy to this problem. Our scheme requires the eigen-decomposition
of a covariance matrix and is as computationally efficient as standard
PCA. We demonstrate some of its favorable properties on robust subspace
estimation
MAGMA: Multi-level accelerated gradient mirror descent algorithm for large-scale convex composite minimization
Composite convex optimization models arise in several applications, and are
especially prevalent in inverse problems with a sparsity inducing norm and in
general convex optimization with simple constraints. The most widely used
algorithms for convex composite models are accelerated first order methods,
however they can take a large number of iterations to compute an acceptable
solution for large-scale problems. In this paper we propose to speed up first
order methods by taking advantage of the structure present in many applications
and in image processing in particular. Our method is based on multi-level
optimization methods and exploits the fact that many applications that give
rise to large scale models can be modelled using varying degrees of fidelity.
We use Nesterov's acceleration techniques together with the multi-level
approach to achieve convergence rate, where
denotes the desired accuracy. The proposed method has a better
convergence rate than any other existing multi-level method for convex
problems, and in addition has the same rate as accelerated methods, which is
known to be optimal for first-order methods. Moreover, as our numerical
experiments show, on large-scale face recognition problems our algorithm is
several times faster than the state of the art
A Unified Framework for Compositional Fitting of Active Appearance Models
Active Appearance Models (AAMs) are one of the most popular and
well-established techniques for modeling deformable objects in computer vision.
In this paper, we study the problem of fitting AAMs using Compositional
Gradient Descent (CGD) algorithms. We present a unified and complete view of
these algorithms and classify them with respect to three main characteristics:
i) cost function; ii) type of composition; and iii) optimization method.
Furthermore, we extend the previous view by: a) proposing a novel Bayesian cost
function that can be interpreted as a general probabilistic formulation of the
well-known project-out loss; b) introducing two new types of composition,
asymmetric and bidirectional, that combine the gradients of both image and
appearance model to derive better conver- gent and more robust CGD algorithms;
and c) providing new valuable insights into existent CGD algorithms by
reinterpreting them as direct applications of the Schur complement and the
Wiberg method. Finally, in order to encourage open research and facilitate
future comparisons with our work, we make the implementa- tion of the
algorithms studied in this paper publicly available as part of the Menpo
Project.Comment: 39 page
A survey on mouth modeling and analysis for Sign Language recognition
© 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR
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