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Erschienen in:
An Analysis of Reordering Algorithms to Reduce the Computational Cost of the Jacobi-Preconditioned CG Solver Using High-Precision Arithmetic Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Building Networks for Image Segmentation Using Particle Competition and Cooperation

verfasst von : Fabricio Breve

Erschienen in: Computational Science and Its Applications – ICCSA 2017

Verlag: Springer International Publishing

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Abstract

Particle competition and cooperation (PCC) is a graph-based semi-supervised learning approach. When PCC is applied to interactive image segmentation tasks, pixels are converted into network nodes, and each node is connected to its k-nearest neighbors, according to the distance between a set of features extracted from the image. Building a proper network to feed PCC is crucial to achieve good segmentation results. However, some features may be more important than others to identify the segments, depending on the characteristics of the image to be segmented. In this paper, an index to evaluate candidate networks is proposed. Thus, building the network becomes a problem of optimizing some feature weights based on the proposed index. Computer simulations are performed on some real-world images from the Microsoft GrabCut database, and the segmentation results related in this paper show the effectiveness of the proposed method.

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Vorheriges Kapitel Q-matrix Extraction from Real Response Data Using Nonnegative Matrix Factorizations
Nächstes Kapitel QueueWe: An IoT-Based Solution for Queue Monitoring
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Metadaten
Titel
Building Networks for Image Segmentation Using Particle Competition and Cooperation
verfasst von
Fabricio Breve
Copyright-Jahr
2017
Buch
Computational Science and Its Applications – ICCSA 2017

Print ISBN: 978-3-319-62391-7

Electronic ISBN: 978-3-319-62392-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-62392-4_16