Department of Brain and Cognitive Sciences Department of Earth and Environmental Sciences: Paleomagnetic Research Group MVRL: Multidisciplinary Vision Research Laboratory

An Active Vision Approach to Understanding and Improving Visual Training in the Geosciences


Data Analyses



For this project we have three distinct stages of data processing which we have called the Processing, Visualization, and Analyses stages, as also noted by the links in the navigation bar above. Data processing deals with the videos and imagery from the mobile eye-tracking systems and the panorama capture systems. Data visualization is where we develop tools, diagrams, and methodologies for working with all of our imagery, eventually in the hopes of developing a virtual field-trip experience. The data analyses are where we focus on probability, statistics, data metrics, and machine-learning procedures in the search for patterns and distinctions among, and between, our novice and expert geoscience subjects (observers on the field trip).


Data analyses are currently in-development, but we have begun to research applications of classical Recurrence Analysis, through the work of Ph.D. Candidate Tommy P. Keane. This work is in progess, but some free-to-use code and diagrams can be found below. The most recent publication at ETRA 2014 details the general development of statistical metrics for classical Recurrence Analysis and contrasts it against the trend of applications in eye-tracking analyses which we would like to more precisely refer to as Reoccurrence Analyses. Again, these are works that are still in development, but these introductory publications and the provided code may still prove of some use.


Eye-movement sequence statistics and hypothesis-testing with classical recurrence analysis


Published in the Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA 2014) by the ACM, (Paper Link) DOI: 10.1145/2578153.2578174

Errata: On page 147, left column, second paragraph, the sentence beginning “You will notice further down in Algorithm...” is a typo. This sentence should have been removed between the reviewed and final drafts, since the algorithm referenced was also removed from the paper during the blind review rounds. Sorry for any confusion.

Provided is a zip archive containing open-source (free to use, share, and modify with attribution please, though no assumed affiliation) python code files, an example image, and example data for computing and visualizing Recurrence Analysis and Reoccurrence Analysis as discussed in the publication. The computations are in their own modules so that they can be incorporated into other software. This is a prototype while we are developing optimized Cython code and integrating these methods into our mobile eye-tracking data viewing/analysis framework. Header comments in all files provide email information for sending any questions or comments. Thank you!

Software with Example Data (zip archive)

Implemented in python (RecurrenceReoccurrenceViewer.py), the GUI requires matplotlib, Qt, and PySide, but the data processing code itself is provided as two separate modules which can be used directly. The data is assumed to be integers or floats inside python lists instead of depending upon numpy.
This code has not been optimized and is provided as-is. Feel free to contact Tommy P. Keane for questions, comments, or help.