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Stereo Correspondence in Information Retrieval

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High-Quality Visual Experience

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

Stereo correspondence is a very important problem in information retrieval. Optimal stereo correspondence algorithms are used to generate optimal disparity maps as well as accurate 3-D shapes from 2-D image inputs. Most established algorithms utilise local measurements such as image intensity (or colour) and phase, and then integrate the data from multiple pixels using a smoothness constraint. This strategy applies fixed or adaptive windows to achieve certain performance. To build up appropriate stereo correspondences, a global approach must be implemented in the way that a global energy or cost function is designed by considering template matching, smoothness constraints and/or penalties for data loss (e.g. occlusion). This energy function usually works with optimisation methods like dynamic programming, simulated annealing and graph cuts to reach the correspondence. In this book chapter, some recently developed stereo correspondence algorithms will be summarised. In particular, maximum likelihood estimation-based, segment-based, connectivity-based and wide-baseline stereo algorithms using descriptors will be introduced. Their performance in different image pairs will be demonstrated and compared. Finally, future research developments of these algorithms will be pointed out.

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Author information

Authors and Affiliations

  1. Queen’s University Belfast, Belfast, BT3 9DT, United Kingdom

    Huiyu Zhou

  2. Brunel University, Uxbridge, UB8 3PH, United Kingdom

    Abdul H. Sadka

Authors
  1. Huiyu Zhou
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  2. Abdul H. Sadka
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Editor information

Editors and Affiliations

  1. University of Surrey, Guildford, UK

    Marta Mrak

  2. University of Zagreb, Zagreb, Croatia

    Mislav Grgic

  3. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Murat Kunt

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhou, H., Sadka, A.H. (2010). Stereo Correspondence in Information Retrieval. In: Mrak, M., Grgic, M., Kunt, M. (eds) High-Quality Visual Experience. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12802-8_23

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  • DOI: https://doi.org/10.1007/978-3-642-12802-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12801-1

  • Online ISBN: 978-3-642-12802-8

  • eBook Packages: EngineeringEngineering (R0)

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