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David Clinton
Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation.
Read more
Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation.
Read more
Many of the millions of digital images we're generating need interpretation, but there aren't enough human eyes for the task. This course will teach you how to use Python libraries and deep learning models to automate image segmentation.
You want your application to consume digital images and convert them to usable data, but it's far too time-consuming to do that manually.
In this course, Image Segmentation, you’ll learn to use Python libraries and deep learning models to automate your image interpretation through segmentation. First, you’ll explore using the OpenCV and Pillow libraries. Next, you’ll discover how to fine tune those libraries, including through the use of the watershed algorithm. Finally, you’ll learn how to use the U-Net and Mask R-CNN deep learning models. When you’re finished with this course, you’ll have the skills and knowledge of image segmentation needed to incorporate image interpretation into your application workflow.
Register for this course and see more details by visiting:
OpenCourser.com/course/3v7y3p/image
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What's inside
Syllabus
Course Overview
Image Segmentation Using OpenCV and PIllow
Image Segmentation Using Deep Learning Models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a strong foundation for beginners in image interpretation through segmentation
Employs relevant industry-standard libraries and deep learning models, including OpenCV, Pillow, U-Net, and Mask R-CNN
Develops skills and knowledge in image segmentation, which are highly relevant for automating image interpretation
Practiced by David Clinton, an experienced instructor in the field of image segmentation
Save this course
Save Image Segmentation to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Image Segmentation with these
activities:
Review Python programming fundamentals
Show steps
Refreshing your Python programming skills will strengthen your foundation for understanding the course material and applying image segmentation techniques.
Browse courses on
Python
Show steps
-
Review basic data structures, loops, and conditional statements
-
Practice writing simple Python programs
Read 'Deep Learning with Python' by François Chollet
Show steps
This book provides a detailed overview of deep learning and its applications, offering a solid foundation for understanding image segmentation techniques.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read Chapter 1 to understand the basics of deep learning
-
Work through the exercises in Chapter 2 to gain hands-on experience with deep learning models
-
Complete the practice problems at the end of each section to reinforce your understanding
Connect with experts in image segmentation
Show steps
Seeking guidance from experienced professionals can accelerate your learning and provide valuable insights into industry practices.
Browse courses on
Image Segmentation
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out to your identified mentors and express your interest in learning from them
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete the OpenCV Image Segmentation Tutorial
Show steps
This tutorial provides practical exercises to enhance your skills in image segmentation using OpenCV, a fundamental tool for image processing.
Browse courses on
Image Segmentation
Show steps
-
Follow the tutorial steps to segment an image using basic OpenCV functions
-
Experiment with different parameters to observe the effects on segmentation results
Attend a workshop on deep learning and image segmentation
Show steps
Attending a workshop can provide additional insights, hands-on experience, and networking opportunities, enhancing your learning journey.
Browse courses on
Deep Learning
Show steps
-
Research and identify relevant workshops in your area
-
Register and actively participate in the workshop
Create a Jupyter Notebook demonstrating image segmentation
Show steps
By creating a Jupyter notebook, you can actively apply the image segmentation techniques you learn in the course and showcase your understanding.
Browse courses on
Image Segmentation
Show steps
-
Import the necessary Python libraries and load an image
-
Apply image segmentation techniques to extract meaningful information from the image
-
Visualize the segmentation results using Matplotlib or another visualization tool
Develop a Python application that segments and analyzes images
Show steps
Building a practical application will solidify your understanding of image segmentation and enable you to solve real-world problems using these techniques.
Browse courses on
Image Segmentation
Show steps
-
Define the problem and gather the necessary data
-
Design and implement the image segmentation algorithm
-
Analyze the segmentation results and iterate to improve accuracy
Review Python programming fundamentals
Show steps
Refreshing your Python programming skills will strengthen your foundation for understanding the course material and applying image segmentation techniques.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic data structures, loops, and conditional statements
- Practice writing simple Python programs
Read 'Deep Learning with Python' by François Chollet
Show steps
This book provides a detailed overview of deep learning and its applications, offering a solid foundation for understanding image segmentation techniques.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1 to understand the basics of deep learning
- Work through the exercises in Chapter 2 to gain hands-on experience with deep learning models
- Complete the practice problems at the end of each section to reinforce your understanding
Connect with experts in image segmentation
Show steps
Seeking guidance from experienced professionals can accelerate your learning and provide valuable insights into industry practices.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out to your identified mentors and express your interest in learning from them
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete the OpenCV Image Segmentation Tutorial
Show steps
This tutorial provides practical exercises to enhance your skills in image segmentation using OpenCV, a fundamental tool for image processing.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Follow the tutorial steps to segment an image using basic OpenCV functions
- Experiment with different parameters to observe the effects on segmentation results
Attend a workshop on deep learning and image segmentation
Show steps
Attending a workshop can provide additional insights, hands-on experience, and networking opportunities, enhancing your learning journey.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Research and identify relevant workshops in your area
- Register and actively participate in the workshop
Create a Jupyter Notebook demonstrating image segmentation
Show steps
By creating a Jupyter notebook, you can actively apply the image segmentation techniques you learn in the course and showcase your understanding.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Import the necessary Python libraries and load an image
- Apply image segmentation techniques to extract meaningful information from the image
- Visualize the segmentation results using Matplotlib or another visualization tool
Develop a Python application that segments and analyzes images
Show steps
Building a practical application will solidify your understanding of image segmentation and enable you to solve real-world problems using these techniques.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Define the problem and gather the necessary data
- Design and implement the image segmentation algorithm
- Analyze the segmentation results and iterate to improve accuracy
Career center
Learners who complete Image Segmentation will develop knowledge and skills
that may be useful to these careers:
Marketing Manager
Marketing Manager
Marketing Managers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Marketing Managers. By taking this course, you will gain the skills and knowledge you need to succeed as a Marketing Manager.
Sales Manager
Sales Manager
Sales Managers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Sales Managers. By taking this course, you will gain the skills and knowledge you need to succeed as a Sales Manager.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Data Scientists. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Machine Learning Engineers. By taking this course, you will gain the skills and knowledge you need to succeed as a Machine Learning Engineer.
Business Analyst
Business Analyst
Business Analysts use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Business Analysts. By taking this course, you will gain the skills and knowledge you need to succeed as a Business Analyst.
Software Engineer
Software Engineer
Software Engineers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Software Engineers. By taking this course, you will gain the skills and knowledge you need to succeed as a Software Engineer.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Data Analysts. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Analyst.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Financial Analysts. By taking this course, you will gain the skills and knowledge you need to succeed as a Financial Analyst.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Risk Analysts. By taking this course, you will gain the skills and knowledge you need to succeed as a Risk Analyst.
Product Manager
Product Manager
Product Managers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Product Managers. By taking this course, you will gain the skills and knowledge you need to succeed as a Product Manager.
Operations Manager
Operations Manager
Operations Managers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Operations Managers. By taking this course, you will gain the skills and knowledge you need to succeed as an Operations Manager.
Data Engineer
Data Engineer
Data Engineers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Data Engineers. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Engineer.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Quantitative Analysts. By taking this course, you will gain the skills and knowledge you need to succeed as a Quantitative Analyst.
Actuary
Actuary
Actuaries use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Actuaries. By taking this course, you will gain the skills and knowledge you need to succeed as an Actuary.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers use their knowledge of data segmentation to help businesses make better decisions. This course teaches the fundamentals of image segmentation, which is a key skill for Computer Vision Engineers. By taking this course, you will gain the skills and knowledge you need to succeed as a Computer Vision Engineer.
For more career information including salaries, visit:
OpenCourser.com/course/3v7y3p/image
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Image Segmentation.
This textbook provides a comprehensive and authoritative overview of digital image processing, covering topics such as image acquisition, image enhancement, image restoration, and image segmentation. It valuable reference for students, researchers, and practitioners in the field of image processing.
Save
This textbook provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image formation, feature detection, image segmentation, and object recognition. It valuable resource for students, researchers, and practitioners in the field of computer vision.
Provides a comprehensive overview of image segmentation and object recognition techniques, covering topics such as image representation, feature extraction, segmentation algorithms, and object recognition algorithms. It valuable resource for students, researchers, and practitioners in the field of image processing and computer vision.
Provides a comprehensive overview of deep learning techniques for computer vision, covering topics such as convolutional neural networks, object detection, and image segmentation. It valuable resource for students, researchers, and practitioners in the field of computer vision.
Provides a comprehensive overview of digital image processing using MATLAB, covering topics such as image acquisition, image enhancement, image restoration, and image segmentation. It valuable resource for students, researchers, and practitioners in the field of image processing.
Provides a comprehensive overview of computer vision theory and applications, covering topics such as image formation, image processing, image analysis, and image understanding. It valuable resource for students, researchers, and practitioners in the field of computer vision.
Provides a comprehensive overview of image segmentation algorithms and applications, covering topics such as image segmentation techniques, image segmentation evaluation, and image segmentation applications. It valuable resource for students, researchers, and practitioners in the field of image processing and computer vision.
Provides a comprehensive overview of image segmentation theory and practice, covering topics such as image segmentation techniques, image segmentation evaluation, and image segmentation applications. It valuable resource for students, researchers, and practitioners in the field of image processing and computer vision.
Provides a comprehensive overview of image segmentation tools and techniques, covering topics such as image segmentation algorithms, image segmentation evaluation, and image segmentation applications. It valuable resource for students, researchers, and practitioners in the field of image processing and computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3v7y3p/image
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Time
32 hours
Level
Intermediate
Via
Pluralsight
Instructor
David Clinton
Language
English
Good to know
Provides a strong foundation for beginners in image interpretation through segmentation
Employs relevant industry-standard libraries and deep learning models, including OpenCV, Pillow, U-Net, and Mask R-CNN
Develops skills and knowledge in image segmentation, which are highly relevant for automating image interpretation
Practiced by David Clinton, an experienced instructor in the field of image segmentation
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