7,226 research outputs found
GPstruct: Bayesian structured prediction using Gaussian processes
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct
A new framework for sign language recognition based on 3D handshape identification and linguistic modeling
Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory
conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions
and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method
less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the
face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American
Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of
large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical.
Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for
ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information
specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic
dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens
Joint Regression and Ranking for Image Enhancement
Research on automated image enhancement has gained momentum in recent years,
partially due to the need for easy-to-use tools for enhancing pictures captured
by ubiquitous cameras on mobile devices. Many of the existing leading methods
employ machine-learning-based techniques, by which some enhancement parameters
for a given image are found by relating the image to the training images with
known enhancement parameters. While knowing the structure of the parameter
space can facilitate search for the optimal solution, none of the existing
methods has explicitly modeled and learned that structure. This paper presents
an end-to-end, novel joint regression and ranking approach to model the
interaction between desired enhancement parameters and images to be processed,
employing a Gaussian process (GP). GP allows searching for ideal parameters
using only the image features. The model naturally leads to a ranking technique
for comparing images in the induced feature space. Comparative evaluation using
the ground-truth based on the MIT-Adobe FiveK dataset plus subjective tests on
an additional data-set were used to demonstrate the effectiveness of the
proposed approach.Comment: WACV 201
Continuous variable entanglement dynamics in structured reservoirs
We address the evolution of entanglement in bimodal continuous variable
quantum systems interacting with two independent structured reservoirs. We
derive an analytic expression for the entanglement of formation without
performing the Markov and the secular approximations and study in details the
entanglement dynamics for various types of structured reservoirs and for
different reservoir temperatures, assuming the two modes initially excited in a
twin-beam state. Our analytic solution allows us to identify three dynamical
regimes characterized by different behaviors of the entanglement: the
entanglement sudden death, the non-Markovian revival and the non-secular
revival regimes. Remarkably, we find that, contrarily to the Markovian case,
the short-time system-reservoir correlations in some cases destroy quickly the
initial entanglement even at zero temperature.Comment: 12 pages, 8 figure
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