The use of computer vision techniques in the medical field has been commonplace since the 1960's when image analysis was used for chromosome pairing [1]. The used of these techniques in recent years has grown to encompass endoscopy, tool inventory recording, and surgical tool tracking in vivo.

Given the potentially life altering stakes for these medical applications, a high degree of accuracy is required of any computer vision algorithm. One of the key elements in three-dimensional algorithms is accurately converting image values (in pixel space) into real world Cartesian coordinates. This conversion requires an accurate and straightforward calibration routine in order to identify the correct transformation. Once the calibration is complete it is also important for the developers to know the accuracy.

While there are existing calibration routines which follow a linear (pinhole) model [2,3], these methods do not...

References

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