Displaying similar documents to “Piecewise-polynomial signal segmentation using convex optimization”

Averaging approach to distributed convex optimization for continuous-time multi-agent systems

Wei Ni, Xiaoli Wang (2016)

Kybernetika

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Recently, distributed convex optimization has received much attention by many researchers. Current research on this problem mainly focuses on fixed network topologies, without enough attention to switching ones. This paper specially establishes a new technique called averaging-base approach to design a continuous-time distributed algorithm for convex optimization problem under switching topology. This idea of using averaging was proposed in our earlier works for the consensus problem...

Impulse noise removal based on new hybrid conjugate gradient approach

Morteza Kimiaei, Majid Rostami (2016)

Kybernetika

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Image denoising is a fundamental problem in image processing operations. In this paper, we present a two-phase scheme for the impulse noise removal. In the first phase, noise candidates are identified by the adaptive median filter (AMF) for salt-and-pepper noise. In the second phase, a new hybrid conjugate gradient method is used to minimize an edge-preserving regularization functional. The second phase of our algorithm inherits advantages of both Dai-Yuan (DY) and Hager-Zhang (HZ) conjugate...

Innovative applications of associative morphological memories for image processing and pattern recognition.

Manuel Graña, Peter Sussner, Gerhard Ritter (2003)

Mathware and Soft Computing

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Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyperspectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on...

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

Ewa Świercz (2010)

International Journal of Applied Mathematics and Computer Science

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A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes,...

Solving convex program via Lagrangian decomposition

Matthias Knobloch (2004)

Kybernetika

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We consider general convex large-scale optimization problems in finite dimensions. Under usual assumptions concerning the structure of the constraint functions, the considered problems are suitable for decomposition approaches. Lagrangian-dual problems are formulated and solved by applying a well-known cutting-plane method of level-type. The proposed method is capable to handle infinite function values. Therefore it is no longer necessary to demand the feasible set with respect to the...