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Hi,
This is a large sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=22QoxEwMBQA> The forest
This image computing looks like SLAM (Simultaneous Localization and
Mapping) method. We obtain both the location (trajectory) and the
visible relief (3D depth map). However, we're dealing with a monocular
(single camera) image sequence, not a stereoscopic (human vision) one.
The sequence is constituted of 10 000 images at 60 frames per second.
It is both large and high-resolution.
Here's the drone's trajectory in space: <https://skfb.ly/pDCwR>
Best regards,
--
<https://eureka.atari.org/>
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Hi,
Francois LE COAT writes:
> This is a large sequence of images from a drone in the forest:
>
> <https://www.youtube.com/watch?v=22QoxEwMBQA> The forest
>
> This image computing looks like SLAM (Simultaneous Localization and
> Mapping) method. We obtain both the location (trajectory) and the
> visible relief (3D depth map). However, we're dealing with a monocular
> (single camera) image sequence, not a stereoscopic (human vision) one.
>
> The sequence is constituted of 10 000 images at 60 frames per second.
> It is both large and high-resolution.
>
> Here's the drone's trajectory in space: <https://skfb.ly/pDCwR>
This is a sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=Kt0mkFYX45A> Drone
The drone flies very slowly compared to the acquisition rate of 30
frames per second. This makes it much easier to observe the trajectory
and visible depth. The successive images in the sequence are highly
correlated. It is not necessary to frequently reset the reference image
selection over time, as the correlation between this image and
subsequent images remains well above the 60% threshold chosen for
resetting.
Best regards,
--
<https://eureka.atari.org/>
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Hi,
Francois LE COAT writes:
> This is a sequence of images from a drone in the forest:
>
> <https://www.youtube.com/watch?v=Kt0mkFYX45A> Drone
>
> The drone flies very slowly compared to the acquisition rate of 30
> frames per second. This makes it much easier to observe the trajectory
> and visible depth. The successive images in the sequence are highly
> correlated. It is not necessary to frequently reset the reference image
> selection over time, as the correlation between this image and
> subsequent images remains well above the 60% threshold chosen for
> resetting.
Here's a sequence of images from a drone in the forest:
<https://www.youtube.com/watch?v=hFcDUU626po> Swedish woods
This image computing called 3D Optical Inertia looks like SLAM
(Simultaneous Localization and Mapping) method. We obtain both
the location (trajectory) and the visible relief (3D depth map).
However, we're dealing with a monocular (single camera) image
sequence, not a stereoscopic (human vision) one.
Here is the spline trajectory in space: <https://skfb.ly/pFx6u>
Best regards,
--
<https://eureka.atari.org/>
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Hi,
Here is a sequence of images of a ballad in the forest. This scene is
observed by a tracking drone...
<https://www.youtube.com/watch?v=b4aTYv7A1lM>
The camera's movement is estimated in the images using a projective
dominant motion measurement. The presence of a man in the image sequence
does not interfere with trajectory estimation, because the character
occupies a position in the field of vision that is not dominant. The
dominant motion corresponds to the scrolling of the scenery, that is
the movement of an observing camera relative to the forest.
Best regards,
--
<https://eureka.atari.org/>
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Hi,
Here is the flight of a drone through the forest...
<https://www.youtube.com/watch?v=-RefA0o2wkE>
What is "temporal disparity"?
In the context of stereoscopic vision or image matching, disparity
(typically spatial) measures the difference in position of a single
point across two images (e.g., left and right).
When applied to the temporal domain, temporal disparity therefore
refers to:
- The positional difference of a point or feature between two
consecutive images in a video sequence, reflecting motion or
temporal change.
- A metric used in computer vision to estimate optical-flow or
3D structure from image sequences.
Measuring optical-flow between successive images matched over time
— by estimating projective dominant motion — reveals "tem
poral
disparity".
Best regards,
--
François LE COAT
<https://eureka.atari.org/>
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