17,736 research outputs found
Accounting for model error in Tempered Ensemble Transform Particle Filter and its application to non-additive model error
In this paper, we trivially extend Tempered (Localized) Ensemble Transform
Particle Filter---T(L)ETPF---to account for model error. We examine T(L)ETPF
performance for non-additive model error in a low-dimensional and a
high-dimensional test problem. The former one is a nonlinear toy model, where
uncertain parameters are non-Gaussian distributed but model error is Gaussian
distributed. The latter one is a steady-state single-phase Darcy flow model,
where uncertain parameters are Gaussian distributed but model error is
non-Gaussian distributed. The source of model error in the Darcy flow problem
is uncertain boundary conditions. We comapare T(L)ETPF to a Regularized
(Localized) Ensemble Kalman Filter---R(L)EnKF. We show that T(L)ETPF
outperforms R(L)EnKF for both the low-dimensional and the high-dimensional
problem. This holds even when ensemble size of TLETPF is 100 while ensemble
size of R(L)EnKF is greater than 6000. As a side note, we show that TLETPF
takes less iterations than TETPF, which decreases computational costs; while
RLEnKF takes more iterations than REnKF, which incerases computational costs.
This is due to an influence of localization on a tempering and a regularizing
parameter
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Simultaneous localization and map-building using active vision
An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
- …