Image segmentation for complex natural scenes

Albert P. Choo, Anthony J. Maeder, Binh Pham

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


An algorithm suitable for segmenting complex textured images of natural scenes is described. There are three stages of processing: defining starting regions; merging similar regions; and refining the boundaries of regions. Starting and merged regions are constructed from fixed size subimages (blocks) to permit the use of local statistical properties of pixel intensities. The choice of block size is made automatically using a similarity test on the statistical properties for randomly chosen blocks of different sizes. The algorithm has performed favourably on typical images of natural scenes using only a few standard texture measures. It uses two simple texture measures, mean and standard deviation, and from the tests performed, these measures were sufficient in picking out the required features. Another measure, contrast, was added to pick out other more obscure regions.

Original languageEnglish
Pages (from-to)155-163
Number of pages9
JournalImage and Vision Computing
Issue number2
Publication statusPublished - May 1990
Externally publishedYes


  • region growing
  • segmentation
  • texture


Dive into the research topics of 'Image segmentation for complex natural scenes'. Together they form a unique fingerprint.

Cite this