Face detection

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Face detection Tuck N.

IDEF0 decomposition

Face detection’s approaches Contour images analysis Intensity and colors analisys Adaptive classifier

Contour images analysis

Face detection’s approaches

Face detection’s approaches Skin color’s cluster

Adaptive classifier

Adaptive classifier

AdaBoost AdaBoost is an algorithm for constructing a “strong” classiﬁer as linear combination f(x) =αtht(x) of “simple” “weak” classiﬁers ht(x) : X -> {−1,+1}.

Rapid Object Detection using a Boosted Cascade of Simple Features Integral Images

Algorithm workflow

Classifier’s algorithm workflow

OWL model We can use OWL model to store some data, used in our application. For example, I’ve stored maximum and minimum area of faces, that should be detected. According to w3c standarts: <owl:Restriction> <owl:onPropertyrdf:resource="#minWidth" /> <owl:minCardinalityrdf:datatype="&xsd;nonNegativeInteger">20</owl:minCardinality> </owl:Restriction> <owl:Restriction> <owl:onPropertyrdf:resource="#maxWidth" /> <owl:maxCardinalityrdf:datatype="&xsd;nonNegativeInteger">300</owl:maxCardinality> </owl:Restriction>

OWL and C# interaction We can use standartXmlDocumentReader to parse OWL model. Also we can use LINQ to parse this model.

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Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

Elevate Developer Efficiency & build GenAI Application with Amazon Q

Elevate Developer Efficiency & build GenAI Application with Amazon Q

Elevate Developer Efficiency & build GenAI Application with Amazon Q

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

AWS Community Day CPH - Three problems of Terraform

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2's API Vision: Unifying Control, Empowering Developers

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

ICT role in 21st century education and its challenges

ICT role in 21st century education and its challenges

ICT role in 21st century education and its challenges

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Corporate and higher education May webinar.pptx

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Face detection

1. Face detection Tuck N.

2. IDEF0 decomposition

3. Face detection’s approaches Contour images analysis Intensity and colors analisys Adaptive classifier

4. Contour images analysis

5. Face detection’s approaches

6. Face detection’s approaches Skin color’s cluster

7. Adaptive classifier

8. Adaptive classifier

9. AdaBoost AdaBoost is an algorithm for constructing a “strong” classiﬁer as linear combination f(x) =αtht(x) of “simple” “weak” classiﬁers ht(x) : X -> {−1,+1}.

10. Rapid Object Detection using a Boosted Cascade of Simple Features Integral Images

11. Algorithm workflow

12. Classifier’s algorithm workflow

13. OWL model We can use OWL model to store some data, used in our application. For example, I’ve stored maximum and minimum area of faces, that should be detected. According to w3c standarts: <owl:Restriction> <owl:onPropertyrdf:resource="#minWidth" /> <owl:minCardinalityrdf:datatype="&xsd;nonNegativeInteger">20</owl:minCardinality> </owl:Restriction> <owl:Restriction> <owl:onPropertyrdf:resource="#maxWidth" /> <owl:maxCardinalityrdf:datatype="&xsd;nonNegativeInteger">300</owl:maxCardinality> </owl:Restriction>

14. OWL and C# interaction We can use standartXmlDocumentReader to parse OWL model. Also we can use LINQ to parse this model.