SlideShare a Scribd company logo
1 of 15
Enhanced Watershed Image Processing Segmentation Aamir Shahzad CIIT/SP05-MCS-002/WAH
Abstract ,[object Object],[object Object],[object Object]
What is Watershed  Segmentation? ,[object Object],[object Object],[object Object]
Marker-Controlled Watershed ,[object Object],[object Object],[object Object]
Proposed system – enhanced watershed segmentation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithms used in main algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Proposed algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example 1 Original Image Simple Watershed Result Marker-Controlled Watershed Result My Watershed Result
Example 2 Original Image Marker-Controlled Watershed Result My Watershed Result
Example 3 Original Image Marker-Controlled Watershed Result My Watershed Result
Example 4 Original Image Marker-Controlled Watershed Result My Watershed Result
Note: Results approximate 1% 20% 20% 0% 6 1% 30% 30% 0% 5 1% 70% 70% 0% 4 1% 10% 50% 40% 3 3% 20% 70% 50% 2 My watershed Watershed 1% 10% 80% 70% 1 Fault   Enhancement  Image No
2% 30% 30% 0% 12 1% 705 70% 0% 11 1% 90% 90% 0% 10 2% 25% 30% 5% 9 1% 3% 53% 50% 8 My watershed Watershed 1% 70% 70% 0% 7 Fault   Enhancement  Image No
 <<   The End  >>  2% 36% 54% 18% Average  10% 0% 25% 30% 15 1% 90% 90% 0 % 14 My watershed Watershed 1% 5% 35% 30% 13 Fault   Enhancement  Image No

More Related Content

Viewers also liked

Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Kevin Keraudren
 
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...Habibur Rahman
 
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
 
Marker Controlled Segmentation Technique for Medical application
Marker Controlled Segmentation Technique for Medical applicationMarker Controlled Segmentation Technique for Medical application
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
 
Neural Network Based Brain Tumor Detection using MR Images
Neural Network Based Brain Tumor Detection using MR ImagesNeural Network Based Brain Tumor Detection using MR Images
Neural Network Based Brain Tumor Detection using MR ImagesAisha Kalsoom
 
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSING
CANCER  CELL  DETECTION USING DIGITAL IMAGE PROCESSINGCANCER  CELL  DETECTION USING DIGITAL IMAGE PROCESSING
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSINGkajikho9
 
Paper id 212014108
Paper id 212014108Paper id 212014108
Paper id 212014108IJRAT
 
Biomedical image processing ppt
Biomedical image processing pptBiomedical image processing ppt
Biomedical image processing pptPriyanka Goswami
 

Viewers also liked (11)

Myelin Matlab Analysis
Myelin Matlab AnalysisMyelin Matlab Analysis
Myelin Matlab Analysis
 
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
 
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...
Segmentation of Color Image using Adaptive Thresholding and Masking with Wate...
 
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...
 
Marker Controlled Segmentation Technique for Medical application
Marker Controlled Segmentation Technique for Medical applicationMarker Controlled Segmentation Technique for Medical application
Marker Controlled Segmentation Technique for Medical application
 
Segmentation Techniques -II
Segmentation Techniques -IISegmentation Techniques -II
Segmentation Techniques -II
 
Neural Network Based Brain Tumor Detection using MR Images
Neural Network Based Brain Tumor Detection using MR ImagesNeural Network Based Brain Tumor Detection using MR Images
Neural Network Based Brain Tumor Detection using MR Images
 
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSING
CANCER  CELL  DETECTION USING DIGITAL IMAGE PROCESSINGCANCER  CELL  DETECTION USING DIGITAL IMAGE PROCESSING
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSING
 
Medical Image Processing
Medical Image ProcessingMedical Image Processing
Medical Image Processing
 
Paper id 212014108
Paper id 212014108Paper id 212014108
Paper id 212014108
 
Biomedical image processing ppt
Biomedical image processing pptBiomedical image processing ppt
Biomedical image processing ppt
 

Similar to MCS Project - Enhanced Watershed

Estrazione automatica delle linee in un'immagine digitale
Estrazione automatica delle linee in un'immagine digitaleEstrazione automatica delle linee in un'immagine digitale
Estrazione automatica delle linee in un'immagine digitalefrancescapadoin
 
Ijmsr 2016-10
Ijmsr 2016-10Ijmsr 2016-10
Ijmsr 2016-10ijmsr
 
Voxels: Rasterizer manual (1.2)
Voxels: Rasterizer manual (1.2)Voxels: Rasterizer manual (1.2)
Voxels: Rasterizer manual (1.2)Ronny Burkersroda
 
Cis 355 i lab 3 of 6
Cis 355 i lab 3 of 6Cis 355 i lab 3 of 6
Cis 355 i lab 3 of 6helpido9
 
Visualizing the Evolution of Working Sets
Visualizing the Evolution of Working SetsVisualizing the Evolution of Working Sets
Visualizing the Evolution of Working SetsRoberto Minelli
 
MODULE III.pptx
MODULE III.pptxMODULE III.pptx
MODULE III.pptxSangeeth39
 
Basic Garbage Collection Techniques
Basic  Garbage  Collection  TechniquesBasic  Garbage  Collection  Techniques
Basic Garbage Collection TechniquesAn Khuong
 
Proyecto parcial ii_grupo2.docx
Proyecto parcial ii_grupo2.docxProyecto parcial ii_grupo2.docx
Proyecto parcial ii_grupo2.docxLuisCuevaFlores
 
Simple Pendulum Experiment and Automatic Survey Grading using Computer Vision
Simple Pendulum Experiment and Automatic Survey Grading using Computer VisionSimple Pendulum Experiment and Automatic Survey Grading using Computer Vision
Simple Pendulum Experiment and Automatic Survey Grading using Computer VisionAnish Patel
 
Workshop 19: ReactJS Introduction
Workshop 19: ReactJS IntroductionWorkshop 19: ReactJS Introduction
Workshop 19: ReactJS IntroductionVisual Engineering
 
JonathanWestlake_ComputerVision_Project1
JonathanWestlake_ComputerVision_Project1JonathanWestlake_ComputerVision_Project1
JonathanWestlake_ComputerVision_Project1Jonathan Westlake
 
JonathanWestlake_ComputerVision_Project2
JonathanWestlake_ComputerVision_Project2JonathanWestlake_ComputerVision_Project2
JonathanWestlake_ComputerVision_Project2Jonathan Westlake
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
 
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...IJCSEA Journal
 
Spatial domain filtering.ppt
Spatial domain filtering.pptSpatial domain filtering.ppt
Spatial domain filtering.pptssuser4bbfb1
 
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSING
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGLAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSING
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGPriyanka Rathore
 
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...IJITCA Journal
 

Similar to MCS Project - Enhanced Watershed (20)

Estrazione automatica delle linee in un'immagine digitale
Estrazione automatica delle linee in un'immagine digitaleEstrazione automatica delle linee in un'immagine digitale
Estrazione automatica delle linee in un'immagine digitale
 
Ijmsr 2016-10
Ijmsr 2016-10Ijmsr 2016-10
Ijmsr 2016-10
 
Voxels: Rasterizer manual (1.2)
Voxels: Rasterizer manual (1.2)Voxels: Rasterizer manual (1.2)
Voxels: Rasterizer manual (1.2)
 
Cis 355 i lab 3 of 6
Cis 355 i lab 3 of 6Cis 355 i lab 3 of 6
Cis 355 i lab 3 of 6
 
Visualizing the Evolution of Working Sets
Visualizing the Evolution of Working SetsVisualizing the Evolution of Working Sets
Visualizing the Evolution of Working Sets
 
MODULE III.pptx
MODULE III.pptxMODULE III.pptx
MODULE III.pptx
 
Basic Garbage Collection Techniques
Basic  Garbage  Collection  TechniquesBasic  Garbage  Collection  Techniques
Basic Garbage Collection Techniques
 
Proyecto parcial ii_grupo2.docx
Proyecto parcial ii_grupo2.docxProyecto parcial ii_grupo2.docx
Proyecto parcial ii_grupo2.docx
 
Simple Pendulum Experiment and Automatic Survey Grading using Computer Vision
Simple Pendulum Experiment and Automatic Survey Grading using Computer VisionSimple Pendulum Experiment and Automatic Survey Grading using Computer Vision
Simple Pendulum Experiment and Automatic Survey Grading using Computer Vision
 
Introduction to fem
Introduction to femIntroduction to fem
Introduction to fem
 
Workshop 19: ReactJS Introduction
Workshop 19: ReactJS IntroductionWorkshop 19: ReactJS Introduction
Workshop 19: ReactJS Introduction
 
Chapter 5
Chapter 5Chapter 5
Chapter 5
 
JonathanWestlake_ComputerVision_Project1
JonathanWestlake_ComputerVision_Project1JonathanWestlake_ComputerVision_Project1
JonathanWestlake_ComputerVision_Project1
 
JonathanWestlake_ComputerVision_Project2
JonathanWestlake_ComputerVision_Project2JonathanWestlake_ComputerVision_Project2
JonathanWestlake_ComputerVision_Project2
 
4 image enhancement in spatial domain
4 image enhancement in spatial domain4 image enhancement in spatial domain
4 image enhancement in spatial domain
 
Machine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural NetworksMachine Learning - Introduction to Convolutional Neural Networks
Machine Learning - Introduction to Convolutional Neural Networks
 
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...
AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USIN...
 
Spatial domain filtering.ppt
Spatial domain filtering.pptSpatial domain filtering.ppt
Spatial domain filtering.ppt
 
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSING
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSINGLAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSING
LAPLACE TRANSFORM SUITABILITY FOR IMAGE PROCESSING
 
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...
INTRODUCING THE CONCEPT OF INFORMATION PIXELS AND THE SIPA (STORING INFORMATI...
 

Recently uploaded

Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 

Recently uploaded (20)

Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 

MCS Project - Enhanced Watershed

  • 1. Enhanced Watershed Image Processing Segmentation Aamir Shahzad CIIT/SP05-MCS-002/WAH
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Example 1 Original Image Simple Watershed Result Marker-Controlled Watershed Result My Watershed Result
  • 10. Example 2 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 11. Example 3 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 12. Example 4 Original Image Marker-Controlled Watershed Result My Watershed Result
  • 13. Note: Results approximate 1% 20% 20% 0% 6 1% 30% 30% 0% 5 1% 70% 70% 0% 4 1% 10% 50% 40% 3 3% 20% 70% 50% 2 My watershed Watershed 1% 10% 80% 70% 1 Fault Enhancement Image No
  • 14. 2% 30% 30% 0% 12 1% 705 70% 0% 11 1% 90% 90% 0% 10 2% 25% 30% 5% 9 1% 3% 53% 50% 8 My watershed Watershed 1% 70% 70% 0% 7 Fault Enhancement Image No
  • 15.  << The End >>  2% 36% 54% 18% Average 10% 0% 25% 30% 15 1% 90% 90% 0 % 14 My watershed Watershed 1% 5% 35% 30% 13 Fault Enhancement Image No