Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers

  • Daesik Jang
  • Gregor Miller
  • Sidney Fels
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)

Abstract

The majority of vision research focusses on advancing technical methods for image analysis, with a coupled increase in complexity and sophistication. The problem of providing access to these sophisticated techniques is largely ignored, leading to a lack of application by mainstream applications. We present a feature-based clustering segmentation algorithm with novel modifications to fit a developer-centred abstraction. This abstraction acts as an interface which accepts a description of segmentation in terms of properties (colour, intensity, texture, etc.), constraints (size, quantity) and priorities (biasing a segmentation). This paper discusses the modifications needed to fit the algorithm into the abstraction, which conditions of the abstraction it supports, and results of the various conditions demonstrating the coverage of the segmentation problem space. The algorithm modification process is discussed generally to help other researchers mould their algorithms to similar abstractions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daesik Jang
    • 1
  • Gregor Miller
    • 2
  • Sidney Fels
    • 2
  1. 1.Kunsan National UniversityGunsanSouth Korea
  2. 2.Human Communication Technologies LaboratoryUniversity of British ColumbiaVancouverCanada

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