Hi,
I've recently watched an interview to GS-15 CIA Officer John Ramirez. He makes an example of a theoretical FOIA request about crop circles, which reminded me of this "mystery". While I have no answer to what their origin could be, I wonder if these could be the equivalent of what fiducial markers are in electronics. In my lab experience, I had to train a die bonder machine. In order to place chips on the board correctly, the board is equipped with little markers in the shapes of crosses, crescents and the like. The AI agent looks for those markers to find the right spot. Thus, I would argue that if you want to identify a place on Earth but you don't have access to e.g. GPS, one way to do it would be to search for fiducials on the surface and triangulate the position. This does not imply we are dealing with alien technology (actually to me this suggests a very human origin) but it may be a step forward away from the trope of two elders with a wooden board and ropes.
Hello all, does anyone know if by implanting fiducial markers within the lungs makes a change in breathing in any way, even if that change is small? I’ve been looking for information on this via the manufacture documentation for fiducial markers and multiple studies but I don’t see anything addressing this.
Thank you! :)
https://github.com/markisus/pytagmapper
I made this project to help me establish ground truth for a more complicated computer vision system. It uses gaussian belief propagation together with manifold optimization on SE2 and SE3 to piece together a global map from multiple local camera images.
The example map scene from the repo turned out to be accurate within a millimeter based on some measurements I made with a caliper.
I've also released an inside out tracker which operates on a built map and video stream.
Hi all --
I asked a question about large ReSampFID residuals a while back in this thread: https://www.reddit.com/r/MicMac/comments/pbg72w/suggested_workflow_for_georeferenced_aerial_scans/
But I never managed to solve the problem. In short, I get huge residuals and distorted 'OIS-Reech' images when I run mm3d ReSampFID "*.tif" 0.012
".*.tif": 2 matches.
"img1.tif": 1 matches.
"img2.tif": 1 matches.
=== RESAMPLE EPIP img1.tif Ker=5 Step=10 SzRed=[1768,1767]=====
=== RESAMPLE EPIP img2.tif Ker=5 Step=10 SzRed=[1768,1767]=====
FOR img1.tif RESIDU 35323.3 Time 131.119
FOR img2.tif RESIDU 35324.9 Time 136.302
I have no calibration report for these images (only focal length) so I estimated the locations of the fiducial marks using the image size (in pixels) and the resolution the analog photos were scanned at. This process (see below) gives me similar values to those on the historic orthoimage tutorial online.
Can anyone suggest some ways I might solve this problem?
P.S. This is the method I used to estimate fiducial mark locations, in case this might the problem.
-
I calculated DPI using the scan resolution of 12 microns as
(12/1,000,000)*39.37
and then found the inverse of this to give a DPI of2116.671 -
Next I measured vertical and horizontal distances (in pixels) between the four corner fiducials and computed x,y coordinates for each fiducial using these distances and the DPI.
To give one example, for fiducial #1, the x distance between fiducial #1 (bottom left) and fiducial #4 (bottom right) is -17664 px and the y distance from fiducial #1 to fiducial #3 (top left) is -17660 px.
I then divided these values by 2 and calculated (-8832/2116.671)*25.4 = -105.938 for the x coord and (-8830/2116.671)*25.4 = -105.960 for the y coord.
- I translated the fiducial coordinates to a top left origin where the Y axis is inverted
(Yinv=-Y)
and coordinates translated(X'=X-X_min and Y'=Yinv-Yinv_min)
This gives me the output:
| fiducial # | X | Y | Yinv | X' | Y' |
|---|---|---|---|---|---|
| 1 | -105.984 | -105.960 | 105.960 | 0.016 | 211.960 |
| 2 | 105.966 | 105.948 | -105.948 | 211.966 | 0.052 |
| 3 | -105.966 | -105.960 | 105.960 | 0.034 | 211.960 |
| 4 | 105.984 | 105.948 | -105.948 | 211.984 | 0.052 |
| -106 (X_min) | -106 (Yinv_min) |
- Finally, I estimated the mid-side fiducials by
X'max-X'minand `Y'm
The bar in question has an apartment in the back that if purchased would be occupied by the owner/operator. Is it best to purchase the property with one LLC, purchase the bar with another, and then have the Property LLC lease to the Bar LLC the Property LLC rent the apartment to the owner. This would preserve liability shields (I assume) and perhaps even is advantageous for insurance? My question - is there a tax reason to do this as well? Presuming the Bar, LLC pays the owner a salary, and if both the Bar LLC and the renting owner are paying rent to the Property, LLC are there any other reasons to do this from a tax reduction stand point? In other words what is the best way to structure this type of purchase and ownership? Would it be significantly different if there were partners on the building but not in the bar? BTW this Reddit has been super helpful already to me and I want to say thanks in advance.
I was talking to someone earlier today who wants to track several objects in a room and they're looking to create different markers that can be attached to objects and they may or may not be placed out of view before leaving. Basically they want to be able to inventory whatever objects enter and leave. I want to create a bunch of unique markers/stickers which are roughly the same size, two-tone (B&W or some other high contrast combo), will be in different lighting conditions and may be partially occluded at times. I know there's more to figuring out the positional tracking for the scenario where they're fully occluded but I'm just focusing on making the markers.
A preliminary search brought me to this paper: Automatic generation and detection of highly reliable fiducial markers under occlusion which describes how they made the markers which are currently implemented in the OpenCV ArUco module.
After looking at this paper: Effects of rotation and systematic occlusion on fiducial marker recognition it looks like CALTag is more robust than AprilTag when partially occluded (based on tables 5 and 6).
ARTag, AprilTag and CALTag Fiducial Marker Systems: Comparison in a Presence of Partial Marker Occlusion and Rotation seems to suggest the same thing based on the tables in section 4.
Also it looks like they released AprilTag2 since then, but the paper suggests that they favored detection speed over robustness when the tags are partially occluded (section III A). AprilTag 2: Efficient and robust fiducial detection
It looks like Fourier tags are pretty cool with the ability to have the signal degrade fairly steadily as the tag moves away but the authors state that their "current" (paper was written in 07) tag detection technique doesn't attempt to handle occlusions. Fourier tags: Smoothly degradable fiducial markers for use in human-robot interaction
Interesting and close to what I'm looking for but doesn't seem to focus on or mention occlusion and the markers are grey
... keep reading on reddit ➡
Presentation: https://www.youtube.com/watch?v=hcGg6SFsLJg
This work was accepted to BMVC2020.
Link to our paper: https://www.bmvc2020-conference.com/assets/papers/0890.pdf
E2ETag proposes an end-to-end trainable method for designing, detecting, and enabling fiducial markers with deep learning. Our method is made possible by introducing back-propagatable marker augmentation and superimposition into training. The detector used was a modified DeepLabV3+ encoder which predicts marker's localization, projective pose, and class. The images used for superimposition training were from the ImageNet dataset. Results demonstrate that our method outperforms existing fiducial markers in ideal conditions and especially in the presence of motion blur, contrast fluctuations, noise, and off-axis viewing angles.
Interested local or overseas pi pioneers wishing to deal with pi crypto can contact me personally on 57248622 or mail me on pimauritius940@gmail.com
Presentation: https://www.youtube.com/watch?v=hcGg6SFsLJg
This work was accepted to BMVC2020.Link to our paper: https://www.bmvc2020-conference.com/assets/papers/0890.pdf
E2ETag proposes an end-to-end trainable method for designing, detecting, and enabling fiducial markers with deep learning. Our method is made possible by introducing back-propagatable marker augmentation and superimposition into training. The detector used was a modified DeepLabV3+ encoder which predicts marker's localization, projective pose, and class. The images used for superimposition training were from the ImageNet dataset. Results demonstrate that our method outperforms existing fiducial markers in ideal conditions and especially in the presence of motion blur, contrast fluctuations, noise, and off-axis viewing angles.
