I decided to shift my focus from finding missing persons with Loc8 to locating important objects with Loc8. Using the dataset collected during the GCP demonstration a week ago I used Loc8 to find the GCPs. Since these objects were either bright yellow/black/white or black/neon pink/numbered they stood out from their surroundings, and their location was already known. I began by finding an image in the dataset that contained both kinds of GCPs used, figure 1.
Figure 1:Scent Image
I then created a color signature for each of the GCPs by using the viewer to sample colors from the scent image, results seen in figures 2 and 3.
Figure 2: Aeropoint color sample
Figure 3: Manual GCP color sample
These color samples are rather large, and the aeropoint set has some strange blues and greens in it. This is where the colors blurred together as they were zoomed in on. This would have likely been avoided if I had gathered the color range from images taken from a less extreme range.
Figure 4: Loc8 window
Figure 4 shows the settings I used for the processing performed. Using these settings, and the two spectral databases Loc8 returned 238 hits. Unfortunately 132 of these were false positives, and almost all of the 106 that contained the GCPs contained false positives as well. This is likely due to the large ranges of colors selected, and "Min Pixels" being set to 1. Even with a large number of false positives it was reassuring that the software was able to find each of the GCPs.
Figure 5
Figure 5 was the best find of the set having no false negatives or no false positives. Figure 6 zooms in on the found GCP and that shows that the marker was found based on one of the white squares on the point.
Figure 6
Figure 7
Figure 7 was another good find by the software, having no false positives, but it did have a few false negatives. These can be more clearly seen in figure 8 below.
Figure 8
These GCPs ended up clustered pretty close together, and the software was able to find 2/5 in the image. I'm unsure of why it was unable to find 2 of the aeropoints, when it was able to find the other 2 in the same image. Similarly I'm not sure why it was unable to find the manual GCP. In practice this would still likely be adequate to find all the GCPs since they're so closely clustered together.
Figure 9
Figure 9 was able to find all 5 GCPs in the image, including manual and aeropoints. However most of the alerts in the image, 9/14, were false positives. Figure 10 more clearly shows four of the five GCPs that the software was able to locate.
Figure 10
Figure 11
Figure 11, similarly to figure 9, located multiple GCPs but provided mostly false positives.
Following tests of the software will be focused on reducing the number of false positives.
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