Light field camera provides 4D information of the light rays, from which the scene depth information can be inferred. The disparity/depth maps calculated from light field data are always noisy with missing and false e...
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ISBN:
(纸本)9781479934331
Light field camera provides 4D information of the light rays, from which the scene depth information can be inferred. The disparity/depth maps calculated from light field data are always noisy with missing and false entries in homogeneous regions or areas where view-dependant effects are present. In this paper we proposed an adaptive guided filtering (AGF) algorithm to get an optimized output disparity/depth map. A guidance image is used to provide the image contour and texture information, the filter is able to preserve the disparity edges, smooth the regions without influence of the image texture, and reject the data entries with low confidence during coefficients regression. Experiment shows AGF is much faster in implementation as compared to other variational or hierarchical based optimization algorithms, and produces competitive visual results.
We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent methods enjoy strong theoretical guarantees and often have better pe...
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ISBN:
(纸本)9781632660244
We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent methods enjoy strong theoretical guarantees and often have better performances than stochastic gradient descent methods in optimizing regularized loss minimization problems. It still lacks of efforts in studying them in a distributed framework. We make a progress along the line by presenting a distributed stochastic dual coordinate ascent algorithm in a star network, with an analysis of the tradeoff between computation and communication. We verify our analysis by experiments on real data sets. Moreover, we compare the proposed algorithm with distributed stochastic gradient descent methods and distributed alternating direction methods of multipliers for optimizing SVMs in the same distributed framework, and observe competitive performances.
This paper studies the design of optimal proper scoring rules when a principal has partial knowledge of an agent’s signal distribution. Recent work [24] characterizes the proper scoring rules that maximize the increa...
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A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence (AI). These almost all result from training flexible algorithms to solve difficult optimization problems spec...
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The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear paramet...
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The original differential evolution algorithm(DE) is a single-population differential evolution algorithm(SPDE).DE converges very quickly,and takes the advantage of *** improved DE has a better performance,but there a...
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ISBN:
(纸本)9781713800361
The original differential evolution algorithm(DE) is a single-population differential evolution algorithm(SPDE).DE converges very quickly,and takes the advantage of *** improved DE has a better performance,but there are premature problems in optimizing complex *** multi-population differential evolution algorithm(MPDE) is proposed to overcome premature problems in this *** optimal substitution strategy(OSS) and the elite immigration strategy(EIS) are studied to maintain the diversity of *** simulation concludes that MPDE converges faster than SPDE in optimizing the ultra-high dimensional problems,and the EIS is superior to the ***,the efficiency of DE is more effective than that of MPDE when the algorithms *** shows that multi-population strategy is a feasible and effective way to the premature problems of DE.
In this article, we introduce an original hybrid quantum-classical algorithm based on a variational quantum algorithm for solving systems of differential equations. The algorithm relies on a spectral method, which inv...
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In many spectral estimation and array processing problems, the process of finding estimates of model parameters often involves the optimisation of a cost function containing multiple peaks and dips. Such non-convex pr...
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ISBN:
(纸本)9781509018918
In many spectral estimation and array processing problems, the process of finding estimates of model parameters often involves the optimisation of a cost function containing multiple peaks and dips. Such non-convex problems are hard to solve using traditional optimisation algorithms developed for convex problems, and computationally intensive grid searches are therefore often used instead. In this paper, we establish an analytical connection between the grid size and the parametrisation of the cost function so that the grid size can be selected as coarsely as possible to lower the computation time. Additionally, we show via three common examples how the grid size depends on parameters such as the number of data points or the number of sensors in DOA estimation. We also demonstrate that the computation time can potentially be lowered by several orders of magnitude by combining a coarse grid search with a local refinement step.
We present a new shape prior formalism for the segmentation of rectified facade images. It combines the simplicity of split grammars with unprecedented expressive power: the capability of encoding simultaneous alignme...
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ISBN:
(纸本)9781467369657
We present a new shape prior formalism for the segmentation of rectified facade images. It combines the simplicity of split grammars with unprecedented expressive power: the capability of encoding simultaneous alignment in two dimensions, facade occlusions and irregular boundaries between facade elements. We formulate the task of finding the most likely image segmentation conforming to a prior of the proposed form as a MAP-MRF problem over a 4-connected pixel grid, and propose an efficient optimization algorithm for solving it. Our method simultaneously segments the visible and occluding objects, and recovers the structure of the occluded facade. We demonstrate state-of-the-art results on a number of facade segmentation datasets.
Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimizatio...
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