1. New horizons in the artificial vision
RETINA
New horizons in the artificial vision
1-Introduzione
2-Caratteristiche
3-Caratteristiche funzionali
4-Case history
5-Conclusioni

2. It has the ability to learn and recognize
What is RETINA?
RETINA is a C/C++ library based on artificial intelligence for digital image processing.
It has the ability to learn and recognize
objects in an image.
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3. On what consists RETINA?
C/C++ library
The library has a complete set of functions to manage all the features of RETINA.
GUI
configure models
manage samples of the image
run SVL
analyze images
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4. Retina Library Library files:
sb.dll (Windows)
sb.so (Linux)
sb.h
It exports a complete set of functions to manage:
image samples
project file
generic ROI
images elaboration
SVL
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5. Retina GUI With the GUI you can train the models.
The result of the training is a project file.
RETINA project file is all you need to process images.
RETINA project file
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6. Library integration Minimal integration:
develop code for images elaboration
use GUI for learning
Full integration:
develop GUI for learning
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7. Image elaboration 1 2 3 4 Load RETINA project file Load image
sb_retina_load()
Load image
sb_image_load()
Create the ROI
sb_roi_create()
Elaborate image
sb_retina_detection()
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8. Never as easy as when you use the perception!
RETINA
Segmentation?
Never as easy as when you use the perception!
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9. Why is it so difficult to achieve?
Segmentation
It means identifying each object in the image .
The human vision system can do it so well and without any effort of will that we believe it is an easy task to perform.
Why is it so difficult to achieve?
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10. Perception Do squares A and B have the same colours?
Then, our brain is deceiving us?
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11. Perception Perception creates great confusion because:
what seems clear to the human eye
is hidden to the image processing algorithms
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12. «The perception is the expectation of finding a model»
What is the perception?
When our brain sees an image, it always tries to simplify it as much as possible
Our perception is always willing to organize what it sees in the most logical and comprehensible set.
So you can say that:
«The perception is the expectation of finding a model»
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13. Perception versus Vision
For a vision system
A and B have the same color.
For a visual perception system
A and B do not have the same color.
The most correct definition of RETINA?
«A visual perception system»
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14. RETINA
Characteristics
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15. Characteristics Generic analysis not dedicated to any specific task
No configuration parameters needed
It learns through the training
Supervised learning (SVL) with human-machine interaction
Multi-models management
Scale management
Collaborating/competing models management
Models perturbations management
Synthesis of multiple shapes in one model without losing the details
Tolerant to: perspective distortion, blur, light change, image contrast, noise, shape deformation
Support for multi thread and multi core processing
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16. How to train RETINA RETINA is trained and ready to be used
The operator creates a set of images, which is representative of models variability
The operator manually selects at least one sample for each model
The operator starts the interactive SVL procedure
RETINA is trained and ready to be used
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17. What is the SVL? . . . it learns! You teach . . . You are the teacher
RETINA is the schoolboy
. . . it learns!
You teach . . .
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18. The operator defines the target: the SVL works in order to achieve it.
The operator’s goal is to have an application which find objects in the images. The target is represented by the objects that the operator selects in the images. In technical language it is called “ground truth”.
The SVL uses the "ground truth" and the background to extract and classify information in order to self-organize knowledge so that the "ground truth" is differentiated from the background.
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19. System requirements Library for x86 architecture
Porting on other architectures and embedded systems is possible
Does not require dedicated hardware (no GPU)
SO: Windows, Linux
Balances RAM/HD according to data base size
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20. Field Applications
Industrial (e.g. object detection, object classification, defects recognition)
Robot vision
Automation
Quality control
Sorting machines
Security (e.g. pedestrian, people counter)
Video Surveillance
AV Image Processing
Automotive (e.g. pedestrian, traffic signal recognition)
ADAS – Advanced Driver Assistance Systems
Intelligent Rearview Monitoring Systems
Driver Monitoring System
Autonomous Driving
Traffic (e.g. vehicle classification, vehicle counting)
Toll collection
Congestion detection
Traffic monitoring
Parking Management
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21. Functional characteristics
RETINA
Functional characteristics
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22. An eye to details but tolerant when needed
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23. Tolerant to perspective distortion
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24. Tolerant to blur
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25. Tolerant to low image contrast
This is a red fish
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26. Tolerant to noise
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27. Tolerant to shape deformation
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28. Robust to partial occlusions
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29. Shadow? No problem!
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30. Tolerant to light change
Fish is moving from sun to shadow
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31. Rotation on 360°
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32. Do you think it’s impossible?
RETINA
Do you think it’s impossible?
Let me try!
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33. Beyond limits of vision
RETINA
Beyond limits of vision