A self-directed method for image segmentation using a modified top-down region dividing approach

Walter S. Wehner, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We present a self-directed method for image segmentation using a modified top-down region dividing (TDRD) approach. The TDRD-based image segmentation method solves some of the issues with histogram and region growing-based segmentation techniques. The process is efficient and achieves proper results without over segmentation or spatial-structure destruction. In this paper, we examine seven user-defined parameters of the method. These parameters are converted from human inputs to values derived from in-class information created by the algorithm allowing for autonomous image segmentation, without the need of human input or feedback. Our new autonomous implementation also reduces the computational complexity of the algorithm. This reduction will produce significant savings for the total number of computations the algorithm needs to perform image segmentation. Experimental results show that the images using these new derived values yield superior results as compared to other methods, including the original TDRD method. We compare our results visually and numerically based on the within-class standard deviation (WCSD) and the number of connected components (NCC).

Original languageEnglish (US)
Article number14550088
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume28
Issue number6
DOIs
StatePublished - Sep 15 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Automatic thresholding
  • Fuzzy logic
  • Image segmentation
  • Region dividing

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