Superpixels using morphology for rock image segmentation

Sree Ramya S.P. Malladi, Sundaresh Ram, Jeffrey J. Rodríguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Detection and segmentation of rocks is an important first task in many applications such as geological analysis, planetary science and mining processes. Rocks are usually segmented using a variety of features such as texture, shading, shape and edges. It is easier to compute these features for rock superpixels rather than every pixel in the image. A superpixel is a group of spatially coherent pixels that form a meaningful homogeneous region, usually belonging to the same object. In this paper, we perform a comparative study of some of the current superpixel algorithms on rock images with regard to their ability to adhere to image boundaries, their speed, and their impact on rock segmentation performance. Also, we propose a new and very simple superpixel algorithm, Superpixels Using Morphology (SUM), which permutes a watershed transformation approach to efficiently generate superpixels. We show that SUM achieves a performance comparable to the recent superpixel algorithms on the rock images.

Original languageEnglish (US)
Title of host publication2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781479940530
StatePublished - 2014
Event2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - San Diego, CA, United States
Duration: Apr 6 2014Apr 8 2014

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation


Other2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014
Country/TerritoryUnited States
CitySan Diego, CA


  • area closing
  • morphology
  • rock particles
  • superpixels
  • watershed segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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