Developed method of creating a high spatial resolution video from a series of panorama-based still images.
Independently implemented dynamic image stitching in C (OpenCV) and integrated into existing software.
Presentation: Simulating High Quality Video from Still Images
1. 1
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HIGH QUALITY
SIMULATED VIDEO
FROM STILL IMAGES
presented by Alexander Chan & Nima Hashemi
Advisor – Dr. Shay Har-Noy | Technical Lead – David
Schmidt
2. |Typical UAV System
1. UAV captures video of ground in real time
2. Data transmitted via airborne modem
3. Data passes through 10 mb/s data link
4. Video received through ground modem and
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viewed
(2)
(3)
(4) (1)
3. |Motivation: Pen or Marker?
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3
Problem:
Detecting small features in
video is difficult.
Resolution:
Use high resolution still
images to create
simulated video.
4. | Comparing Video and Still
Images
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Spatial versus Temporal Resolution
HD Video
1920x1080 = 2 Mp per frame. 3.2 pixel/inch
Still Images
4743 x 3162 = 15 Mp per frame. 7.9 pixel/inch
8. |Image Stitching Visualized
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Extracted Stitched Paired Mosaic FFeeaattuurree (Feature PPooiinnttss R(SeUmRaFin) Mapping (FLANN and Blurring)
Matching &
RANSAC)
A B
A & B
9. |Demonstration
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Zooming into video for visual feature detection
Synchronization between video and image
windows
10. |Current Limitations and Future
Steps
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Increase robustness of stitching algorithm
Increase accuracy of time relation between
video feed and image viewer
Optimize CPU consumption throughout
system
11. |What We Learned
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Image Processing and User Interface
Project Management and Spiral Development
“Integration takes longer than development”
Integrating Open Source Libraries
(OpenCV, IJG Library, and existing EnerView
system)
13. |Acknowledgements
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13
Dr. Shay Har-Noy, Dave Schmidt, Steve
Gardner
Fran Abrams and the ViaSat HR team
OpenCV Community, IJG Community
Kevin, Andy, Brian, Ricky, and our intern family
The Antarctic Penguins
Notas del editor
Let the audience know who we are
Name
Major
Year
Aspirations (tech entrepreneur, business development ?)
COINSTRAINTS: Video feed from UAV inherently mediocre quality:
10 mb/s data link limits video quality
Zooming for video is sub-optimal
Take this sample video frame: With regards to being able to detect small changes in landscape – it’s easy to see that there’s significant room for improvement.
Mention Visionary Steve Gardner- “penguins”
We take the conventional Video Feed and sort of turn it on its head.
Mapping video feed to mosaic coordinates based on timestamp
Displaying selected region in Image Viewer at high resolution and zoom
Mosaic continues to be stitched dynamically as video plays
Arrows represent direction of motion. Note that the small rectangle’s movement translates to the movement the image viewer
Building a dialogue box off of the existing EnerView System
I will go over this briefly.
Start with a directory of images.
Downsample images to decrease computation time.
Extract features
Find matching features between images
RANSAC filters out bad matches
Create homography out of accurate feature relationships
-> this describes the overall translation of one image into another
Finally apply the homography and combine the images
-> through a short mapping and blurring algorithm
Robustness – meaning it can extrapolate relationships between two images even if they are rotated, stretched, zoomed at different angles, etc. As of now, we are simply using a linear approach.
Optimizing – multithreading, further downsampling of images,
Various algorithms
Zooming and aspect ratios
Gained extensive experience in agile development cycle, unit testing, and core development
A static image mosaic approach absolutely allows users to visually detect features more easily.
Ecological surveying
Disaster relief
IED Detection