Teach the Earth > Structural Geology > Teaching Activities > Using the Autocorrelation Function in NIH-Image to Determine Shape-Preferred Orientations

Using the Autocorrelation Function in NIH-Image to Determine Shape-Preferred Orientations

Cameron Davidson
Carleton College
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This activity was selected for the On the Cutting Edge Reviewed Teaching Collection

This activity has received positive reviews in a peer review process involving five review categories. The five categories included in the process are

  • Scientific Accuracy
  • Alignment of Learning Goals, Activities, and Assessments
  • Pedagogic Effectiveness
  • Robustness (usability and dependability of all components)
  • Completeness of the ActivitySheet web page

For more information about the peer review process itself, please see http://serc.carleton.edu/NAGTWorkshops/review.html.

This page first made public: Jun 18, 2004

This material was originally developed as part of the Carleton College Teaching Activity Collection
through its collaboration with the SERC Pedagogic Service.


Students learn how to use the autocorrelation function in NIH-Image to quantify shape-preferred orientations. Students develop understanding of what the autocorrelation function does by using synthetic images and then apply this knowledge to real rocks.

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Undergraduate structural geology course; typically juniors and seniors.

Skills and concepts that students must have mastered

Students should be comfortable using spreadsheets to organize, manipulate, and graph data; basic understanding of shape-preferred orientation in rocks (e.g. foliation).

How the activity is situated in the course

Designed to be completed in a four-hour laboratory period. However, students are given a week to complete the activity.


Content/concepts goals for this activity

  1. Learn how to use NIH-Image to quantify shape-preferred orientations in images.
  2. Discover how autocorrelation images vary with aspect ratio of particles and the amount of preferred orientation (fabric intensity).
  3. Apply this understanding to rocks.

Higher order thinking skills goals for this activity

Collecting and manipulating real data; making decisions on what the data mean.

Other skills goals for this activity

Making figures, writing figure captions, presenting data and interpretations in narrative form.

Description of the activity/assignment

This activity can be completed individually or in small groups. The first part of the activity focuses on exploration and discovery of how autocorrelation function (ACF) images correspond to real images. The students are given six synthetic images with particles of known aspect ratio and shape preferred orientation. They follow a detailed recipe for generating ACF's, construct a figure comparing images and ACF's, and are asked to write a short narrative describing the qualitative relationship between an image and ACF shape. The students then learn how to measure the ellipticity of ACF contours and plot these data to discover the relationship between shape preferred orientation and ACF ellipticity. After completing the discovery part of the activity, the students apply this knowledge to images of thin sections from the Quottoon pluton near Prince Rupert, British Columbia. In addition, they learn how to measure ACF grain size and plot these data and ACF ellipticity as a function of distance from the Coast shear zone, a major oblique-slip shear zone that defines the western border of the Quottoon pluton. Finally, the students are asked to discuss the relationship between shape preferred orientation, ACF grain size, and the Coast shear zone.

Determining whether students have met the goals

The students construct and hand in three figures, complete with figure captions. For each figure, they write a short narrative describing and interpreting the figure.

More information about assessment tools and techniques.

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Supporting references/URLs

Davidson, C., Rosenberg, C., and Schmid, S.M., 1996, Synmagmatic folding of the base of the Bergell pluton, Central Alps: Tectonophysics, v. 265, p. 213-238.

Panozzo Heilbronner, R. 1992. The auto correlation function: an image processing tool for fabric analysis: Tectonophyscis, v. 212, p. 351-370.

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