Written by : Itai Bechor
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Eye Detection in Images Introduction To Computational and biological Vision презентация
Содержание
- 1. Eye Detection in Images Introduction To Computational and biological Vision
- 2. Chapter Headings Introduction The Main algorithm:
- 3. Introduction Detecting Eyes has many applications: For
- 4. Introduction The Eye is Quite Unique Feature
- 5. The Algorithm Diagram Detect face
- 6. Images I work with Black and
- 7. Extraction of the face regions Step 1
- 8. Face Region Extraction
- 9. The Canny Edge Detector I used Gaussian
- 10. The Gaussian Distribution Basic normal distribution skin Non-skin
- 11. Canny Edge Detector Compute gradient of
- 12. Hough Circle Transformation in
- 13. Hough Circle Transformation
- 14. Hough Circle Transformation
- 15. Selecting the Eyes Labeling Function
- 16. Selecting the Eyes Using the Following Methods:
- 17. Experimental Results Good Results:
- 18. Experimental Results
- 19. Experimental Results Bad Result: Hough Didn’t detect eye circles
- 20. Experimental Results Bad Result: Label Function Didn’t detect eyes.
- 21. Conclusion The Algorithm need to be improved
Слайд 1Eye Detection in Images
Introduction To Computational and biological Vision
Lecturer :
Ohad Ben Shahar
Слайд 2Chapter Headings
Introduction
The Main algorithm:
Detecting the face area
Find a good candidates
Find
the most probability For Eyes in The Image
Conclusions and Results
Conclusions and Results
Слайд 3Introduction
Detecting Eyes has many applications:
For Face Recognition
May Be Use By The
Police
In Security Services
Future Use In Computers Security For Login Propses
In Security Services
Future Use In Computers Security For Login Propses
Слайд 4Introduction
The Eye is Quite Unique Feature in the Face
It might be
easy to detect it more than other elements in the face
The Objective is To detect the Closest Area To the eyes or the Eyes
The Objective is To detect the Closest Area To the eyes or the Eyes
Слайд 6Images I work with
Black and white images
Head Images On a
Plain Background
Image resolution of 150x150 to 300x300
Image resolution of 150x150 to 300x300
Слайд 7Extraction of the face regions
Step 1
Input Image
Step
2
Canny Edge detector
Canny Edge detector
Step 3
Calculate the left
and right bound
Слайд 9The Canny Edge Detector
I used Gaussian 5x5 convolution To smooth the
image to clean the noise
Слайд 12
Hough Circle Transformation
in my program : I Find The
Circles In The Image From Radius 1 to width/2.
A circle in 2d is :
The accumulator Holding the Votes For each Radius.
A circle in 2d is :
The accumulator Holding the Votes For each Radius.
Edge point
r
(Xi,Yi)
Слайд 15Selecting the Eyes
Labeling Function That Find the best Match Between
Two Circles In The Eyes
Слайд 16Selecting the Eyes
Using the Following Methods:
Calculate the Distances between each two
circles .
The Slope Between The Two Circles.
The Radius similarity between two circles.
Large Number of circles in the same area
The Slope Between The Two Circles.
The Radius similarity between two circles.
Large Number of circles in the same area
Слайд 21Conclusion
The Algorithm need to be improved
In Order To Improve it :
Need
To Use A Eyes Database
There is special cameras that can detect the eye using an effect called The bright pupil effect .
There is special cameras that can detect the eye using an effect called The bright pupil effect .
