54,860 research outputs found
Optimisation of the weighting functions of an H<sub>â</sub> controller using genetic algorithms and structured genetic algorithms
In this paper the optimisation of the weighting functions for an H<sub>â</sub> controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H<sub>â</sub> controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The paper presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions' orders
Decorrelation and shallow semantic patterns for distributional clustering of nouns and verbs
Distributional approximations to lexical semantics are very useful not only in helping the creation of lexical semantic resources (Kilgariff et al., 2004; Snow et al., 2006), but also when directly applied in tasks that can benefit from large-coverage semantic knowledge such as coreference resolution (Poesio et al., 1998; Gasperin and Vieira, 2004; Versley, 2007), word sense disambiguation (Mc- Carthy et al., 2004) or semantical role labeling (Gordon and Swanson, 2007). We present a model that is built from Webbased corpora using both shallow patterns for grammatical and semantic relations and a window-based approach, using singular value decomposition to decorrelate the feature space which is otherwise too heavily influenced by the skewed topic distribution of Web corpora
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number of salient objects. During training, we convert the
ground-truth rectangular boxes to Gaussian distributions that better capture
the ROI regarding individual salient objects. During inference, the network
predicts Gaussian distributions centered at salient objects with an appropriate
covariance, from which bounding boxes are easily inferred. Notably, our network
performs saliency map prediction without pixel-level annotations, salient
object detection without object proposals, and salient object subitizing
simultaneously, all in a single pass within a unified framework. Extensive
experiments show that our approach outperforms existing methods on various
datasets by a large margin, and achieves more than 100 fps with VGG16 network
on a single GPU during inference
Efficient moving point handling for incremental 3D manifold reconstruction
As incremental Structure from Motion algorithms become effective, a good
sparse point cloud representing the map of the scene becomes available
frame-by-frame. From the 3D Delaunay triangulation of these points,
state-of-the-art algorithms build a manifold rough model of the scene. These
algorithms integrate incrementally new points to the 3D reconstruction only if
their position estimate does not change. Indeed, whenever a point moves in a 3D
Delaunay triangulation, for instance because its estimation gets refined, a set
of tetrahedra have to be removed and replaced with new ones to maintain the
Delaunay property; the management of the manifold reconstruction becomes thus
complex and it entails a potentially big overhead. In this paper we investigate
different approaches and we propose an efficient policy to deal with moving
points in the manifold estimation process. We tested our approach with four
sequences of the KITTI dataset and we show the effectiveness of our proposal in
comparison with state-of-the-art approaches.Comment: Accepted in International Conference on Image Analysis and Processing
(ICIAP 2015
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time
budgets during application. They allow for individual budgets given a priori
for each test example and for anytime prediction, i.e., a possible interruption
at multiple stages during inference while still providing output estimates. Our
approach can therefore tackle the computational costs and energy demands of
DNNs in an adaptive manner, a property essential for real-time applications.
Our Impatient DNNs are based on a new general framework of learning dynamic
budget predictors using risk minimization, which can be applied to current DNN
architectures by adding early prediction and additional loss layers. A key
aspect of our method is that all of the intermediate predictors are learned
jointly. In experiments, we evaluate our approach for different budget
distributions, architectures, and datasets. Our results show a significant gain
in expected accuracy compared to common baselines.Comment: British Machine Vision Conference (BMVC) 201
Contextual cropping and scaling of TV productions
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows
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