23,375 research outputs found
Perception of Motion and Architectural Form: Computational Relationships between Optical Flow and Perspective
Perceptual geometry refers to the interdisciplinary research whose objectives
focuses on study of geometry from the perspective of visual perception, and in
turn, applies such geometric findings to the ecological study of vision.
Perceptual geometry attempts to answer fundamental questions in perception of
form and representation of space through synthesis of cognitive and biological
theories of visual perception with geometric theories of the physical world.
Perception of form, space and motion are among fundamental problems in vision
science. In cognitive and computational models of human perception, the
theories for modeling motion are treated separately from models for perception
of form.Comment: 10 pages, 13 figures, submitted and accepted in DoCEIS'2012
Conference: http://www.uninova.pt/doceis/doceis12/home/home.ph
On the computational complexity of temporal projection and some related problems
One kind of temporal reasoning is temporal projection -the computation of the consequences for a set of events. This problem is related to a number of other temporal reasoning tasks such as story understanding, plan validation, and planning. We show that one particular simple case of temporal projection on partially ordered events turns out to be harder than previously conjectured. However, given the restrictions of this problem, planning and story understanding are easy. Additionally, we show that plan validation, one of the intended applications of temporal projection, is tractable for an even larger class of plans. The incomplete decision procedure for the temporal projection problem that has been proposed by other authors, however, fails to be complete in the case where we have shown plan validation to be tractable
Evaluating Example-based Pose Estimation: Experiments on the HumanEva Sets
We present an example-based approach to pose recovery, using histograms of oriented gradients as image descriptors. Tests on the HumanEva-I and HumanEva-II data sets provide us insight into the strengths and limitations of an example-based approach. We report mean relative 3D errors of approximately 65 mm per joint on HumanEva-I, and 175 mm on HumanEva-II. We discuss our results using single and multiple views. Also, we perform experiments to assess the algorithmâs generalization to unseen subjects, actions and viewpoints. We plan to incorporate the temporal aspect of human motion analysis to reduce orientation ambiguities, and increase the pose recovery accuracy
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
A Dantzig Selector Approach to Temporal Difference Learning
LSTD is a popular algorithm for value function approximation. Whenever the
number of features is larger than the number of samples, it must be paired with
some form of regularization. In particular, L1-regularization methods tend to
perform feature selection by promoting sparsity, and thus, are well-suited for
high-dimensional problems. However, since LSTD is not a simple regression
algorithm, but it solves a fixed--point problem, its integration with
L1-regularization is not straightforward and might come with some drawbacks
(e.g., the P-matrix assumption for LASSO-TD). In this paper, we introduce a
novel algorithm obtained by integrating LSTD with the Dantzig Selector. We
investigate the performance of the proposed algorithm and its relationship with
the existing regularized approaches, and show how it addresses some of their
drawbacks.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Enabling Quality-Driven Scalable Video Transmission over Multi-User NOMA System
Recently, non-orthogonal multiple access (NOMA) has been proposed to achieve
higher spectral efficiency over conventional orthogonal multiple access.
Although it has the potential to meet increasing demands of video services, it
is still challenging to provide high performance video streaming. In this
research, we investigate, for the first time, a multi-user NOMA system design
for video transmission. Various NOMA systems have been proposed for data
transmission in terms of throughput or reliability. However, the perceived
quality, or the quality-of-experience of users, is more critical for video
transmission. Based on this observation, we design a quality-driven scalable
video transmission framework with cross-layer support for multi-user NOMA. To
enable low complexity multi-user NOMA operations, a novel user grouping
strategy is proposed. The key features in the proposed framework include the
integration of the quality model for encoded video with the physical layer
model for NOMA transmission, and the formulation of multi-user NOMA-based video
transmission as a quality-driven power allocation problem. As the problem is
non-concave, a global optimal algorithm based on the hidden monotonic property
and a suboptimal algorithm with polynomial time complexity are developed.
Simulation results show that the proposed multi-user NOMA system outperforms
existing schemes in various video delivery scenarios.Comment: 9 pages, 6 figures. This paper has already been accepted by IEEE
INFOCOM 201
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
This paper deals with using word embedding models to trace the temporal
dynamics of semantic relations between pairs of words. The set-up is similar to
the well-known analogies task, but expanded with a time dimension. To this end,
we apply incremental updating of the models with new training texts, including
incremental vocabulary expansion, coupled with learned transformation matrices
that let us map between members of the relation. The proposed approach is
evaluated on the task of predicting insurgent armed groups based on
geographical locations. The gold standard data for the time span 1994--2010 is
extracted from the UCDP Armed Conflicts dataset. The results show that the
method is feasible and outperforms the baselines, but also that important work
still remains to be done.Comment: to appear in EMNLP 2017 proceeding
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