In our last post, we promised to tell you about some ongoing work. Our current target backgrounds use pattern matching to find and precisely locate calibration points. Given our experiences with GaugeCam testers, we’re looking to simplify the installation and calibration process without sacrificing too much accuracy. Currently in testing is our “stop sign” calibration target. This feature is not yet live in the GRIME2 software, but we are installing these backgrounds in two locations for testing. At both locations we have HOBO water level loggers for comparison. At one location we will have an adjacent bowtie target installed. Our first location was installed yesterday, with deep gratitude to the landowner who is interested in this work. A photo and time-lapse of the installation are below!
Author: Troy
Getting emotional about water level cameras
Hydrologic modelers like to say “all models are wrong, but some are useful.”
And more recently (via @bisnotforbella), “All models are wrong but some I am emotionally attached to.”
Those of us working on GaugeCam have an emotional attachment to bowties. Because bowties are the shapes that have helped us model the real world by relating pixels in an image to real locations in the image scene.
This post is the first in a series that will explain the history of this emotional attachment, and why it may change in the future.
First, a step back to 2009. Here are some of the first calibration target designs we tried. Circles, bowtie-ish circles, and secchi disk shapes. Tossed in with some horizontal lines to test our water line finding algorithms.
Our first testing used 5.5-inch circular patterns. We did this work in the lab and in the field. Below is the Pullen Park site where we eventually installed two columns of circular fiducials.
Along with the calibration shapes, we had to test line find algorithms to detect the water line. We had some hits and some misses, both in the lab and field.
In the images above, you can quickly see why we were looking for calibration patterns other than classic staff gauges. Our low resolution images at the time didn’t help, but it was very clear that the intuitive idea of just putting staff gauges in the image scene for calibration was not our best path forward.
By 2011 we had developed and tested bowtie targets using both benchmark patterns (synthetic water lines) and real water levels in the lab at North Carolina State University. We presented these results at the American Society of Agricultural and Biological Engineers Annual Conference in Louisville, KY.
Honestly, these are great memories. This was my undergraduate research and some of the early research in the Birgand Lab at NCSU. We also had great volunteers on the project, most notably Ken Chapman, who brought the machine vision and software programming skills necessary to create GaugeCam.
Bowties are still central to our work. But be sure to check back to find out how we’re moving GaugeCam forward!
GaugeCam blog – a revival
When the GaugeCam project started back in 2009, we documented much of the work through blog posts.
We’re excited to start blogging again in 2022, as we take GaugeCam in new directions. The photoshopped image below shows a site we’re planning on instrumenting in 2022.