Hyperconnected Attribute Filters Based on k-Flat Zones

Georgios K. Ouzounis*, Michael H.F. Wilkinson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

31 Citations (Scopus)
214 Downloads (Pure)

Abstract

In this paper, we present a new method for attribute filtering, combining contrast and structural information. Using hyperconnectivity based on k-flat zones, we improve the ability of attribute filters to retain internal details in detected objects. Simultaneously, we improve the suppression of small, unwanted detail in the background. We extend the theory of attribute filters to hyperconnectivity and provide a fast algorithm to implement the new method. The new version is only marginally slower than the standard Max-Tree algorithm for connected attribute filters, and linear in the number of pixels or voxels. It is two orders of magnitude faster than anisotropic diffusion. The method is implemented in the form of a filtering rule suitable for handling both increasing (size) and nonincreasing (shape) attributes. We test this new framework on nonincreasing shape filters on both 2D images from astronomy, document processing, and microscopy, and 3D CT scans, and show increased robustness to noise while maintaining the advantages of previous methods.
Original languageEnglish
Pages (from-to)224-239
Number of pages16
JournalIeee transactions on pattern analysis and machine intelligence
Volume33
Issue number2
DOIs
Publication statusPublished - Feb-2011

Keywords

  • Image enhancement
  • object detection
  • hyperconnectivity
  • connectivity
  • Max-Tree
  • document processing
  • anisotropic diffusion
  • attribute filter
  • CONNECTED OPERATORS
  • COMPLETE LATTICES
  • IMAGE-ANALYSIS
  • SEGMENTATION
  • RECONSTRUCTION
  • ALGORITHMS
  • CLASSIFICATION
  • GRANULOMETRIES
  • MORPHOLOGY
  • DIFFUSION

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