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Janani Ravi

In this course, you'll explore the scikit-image Python library which allows you to apply sophisticated image processing techniques to images and to quickly extract important insights or pre-process images for input to machine learning models.

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In this course, you'll explore the scikit-image Python library which allows you to apply sophisticated image processing techniques to images and to quickly extract important insights or pre-process images for input to machine learning models.

In this course, Building Image Processing Applications using scikit-image, you’ll gain an understanding of a few core image processing techniques and see how these techniques can be implemented using the scikit-image Python library.

First, you’ll learn the basics of working with image data represented in the form of multidimensional arrays. Next, you’ll discover to manipulate images using the NumPy package, extract features using block view and pooling techniques, detect edges and lines and find contours in images.

Then, you’ll explore various object and feature detection techniques using the DAISY and HOG algorithms to extract image features, along with using morphological reconstruction to fill holes and find peaks in your images.

Finally, you'll delve into image processing techniques that allow you to segment similar regions in your images and apply complex transformations by exploring the Regional Adjacency Graph data structure to represent image segments.

By the end of this course, you’ll have a better understanding of a range of image processing techniques that you can use on your images, and you’ll be able to implement all of those using scikit-image.

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What's inside

Syllabus

Course Overview
Working with Image Data
Object and Feature Detection
Segmentation and Transformation
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Develops core image processing techniques, a core skill for data scientists, machine learning engineers, and computer vision engineers
Taught by Janani Ravi, who is a recognized figure in image processing and computer vision
Leverages the scikit-image library, a standard tool in image processing and computer vision
Examines a range of image processing techniques that are applicable to various domains
Provides hands-on experience through the use of the scikit-image library
Suitable for beginners with an interest in image processing and computer vision

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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 Building Image Processing Applications Using scikit-image with these activities:
Organize and review course materials
Consolidate and review course materials to strengthen your understanding of key concepts.
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  • Gather all relevant course materials, including lecture notes, assignments, and readings.
  • Organize the materials in a logical order.
  • Review the materials regularly to reinforce your understanding.
Review the book 'Digital Image Processing' by Rafael C. Gonzalez and Richard E. Woods
Expand your knowledge of image processing principles and techniques by studying a comprehensive reference book.
Show steps
  • Read and understand the foundational concepts presented in the book.
  • Study specific image processing algorithms and their applications.
  • Refer to the book for additional insights and problem-solving guidance.
Follow online tutorials on specific image processing tasks
Reinforce your understanding of image processing techniques by following guided tutorials.
Browse courses on Image Processing
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  • Identify and select tutorials that align with your learning goals.
  • Follow the tutorials step-by-step.
  • Experiment with different parameters and techniques.
Five other activities
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Review fundamental image processing concepts
Strengthen your understanding of core image processing concepts to better grasp the techniques covered in the course.
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  • Review image representation and data structures.
  • Study fundamental image transformations, such as filtering and geometric operations.
  • Explore color models and color spaces.
Practice image processing techniques with scikit-image
Apply image processing techniques using scikit-image to solidify your understanding and gain practical experience.
Browse courses on Image Processing
Show steps
  • Import the necessary scikit-image libraries.
  • Load an image and convert it to a NumPy array.
  • Apply image processing techniques such as filtering, edge detection, and segmentation.
  • Visualize the results of your image processing operations.
Build an image processing application using scikit-image
Develop an image processing application using scikit-image to demonstrate your mastery of the library and practical problem-solving skills.
Browse courses on Scikit-Image
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  • Identify a problem or task that can be solved using image processing.
  • Design and develop an algorithm using scikit-image.
  • Implement the algorithm in code.
  • Test and refine your application.
Create a presentation on an image processing technique
Enhance your understanding of an image processing technique by explaining it to others in a presentation.
Browse courses on Image Processing
Show steps
  • Choose an image processing technique and research it thoroughly.
  • Create a presentation outlining the principles and applications of the technique.
  • Practice your presentation and deliver it to an audience.
Attend a workshop on advanced image processing techniques
Participate in a workshop to learn specialized image processing techniques and gain exposure to industry practices.
Browse courses on Image Processing
Show steps
  • Identify and register for a relevant workshop.
  • Attend the workshop and actively participate in discussions.
  • Apply the techniques learned in your image processing projects.

Career center

Learners who complete Building Image Processing Applications Using scikit-image will develop knowledge and skills that may be useful to these careers:
Robotics Software Engineer
Robotics Software Engineers design, develop, and maintain software for robots. They work on a variety of robots, such as industrial robots, medical robots, and military robots. This course may be useful for those interested in becoming a Robotics Software Engineer, as it provides a foundation in image processing techniques that are used in robotics software engineering applications.
Image Processing Engineer
Image Processing Engineers design, develop, and maintain image processing systems. They work on a variety of projects, such as medical imaging systems, industrial inspection systems, and surveillance systems. This course is a good choice for those interested in becoming an Image Processing Engineer, as it provides a foundation in image processing techniques that are used in image processing engineering applications.
Machine Learning Researcher
Machine Learning Researchers design, develop, and evaluate machine learning algorithms. They work on a variety of topics, such as supervised learning, unsupervised learning, and reinforcement learning. This course may be useful for those interested in becoming a Machine Learning Researcher, as it provides a foundation in image processing techniques that are used in machine learning research.
Data Analyst
Data Analysts collect, analyze, and interpret data. They use this knowledge to help businesses understand their customers, make better decisions, and improve their operations. This course helps build a foundation in image processing techniques that are used in various data analytics applications, making it a beneficial course for those interested in data analysis.
Computer Vision Algorithm Architect
Computer Vision Algorithm Architects design and develop algorithms for computer vision systems. They work on a variety of algorithms, such as object detection, image segmentation, and facial recognition. Since this course focuses on image processing applications, it may help you gain skills that can translate to this field and be considered a benefit to your resume when applying for jobs.
Data Scientist
Data Scientists collect, analyze, and interpret data. They use this knowledge to help businesses understand their customers, make better decisions, and improve their operations. This course helps build a foundation in image processing techniques that are used in various data science applications, making it a beneficial course for those interested in data science.
Computer Vision Engineer
Computer Vision Engineers work to verify the performance of complex algorithms and systems. They design, develop, and test computer vision systems with a focus on technology that enables computers to see, analyze, and interpret images or videos in order to automate tasks. Since this course focuses on image processing applications, it may help you gain skills that can translate to this field and be considered a benefit to your resume when applying for jobs.
Quality Assurance Analyst
Quality Assurance Analysts ensure that products and services meet quality standards. They work in a variety of industries, such as manufacturing, software development, and healthcare. This course may be useful for those interested in becoming a Quality Assurance Analyst, as it provides a foundation in image processing techniques that are used in quality assurance applications.
Transportation Engineer
Transportation Engineers plan, design, and maintain transportation systems. They work on a variety of projects, such as highways, railways, and airports. This course may be useful for those interested in becoming a Transportation Engineer, as it provides a foundation in image processing techniques that are used in transportation engineering applications.
Medical Imaging Analyst
Medical Imaging Analysts use imaging techniques to create visual representations of the interior of the body. They work closely with radiologists and other medical professionals to interpret images and diagnose medical conditions. This course is a good choice for those interested in becoming a Medical Imaging Analyst, as it helps build a foundation in image processing techniques that are used in medical imaging applications.
Systems Engineer
Systems Engineers design, develop, and maintain complex systems. They work on a variety of systems, such as computer systems, telecommunications systems, and transportation systems. This course may be useful for those interested in becoming a Systems Engineer, as it provides a foundation in image processing techniques that are used in systems engineering applications.
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning systems. They work on a variety of tasks, such as developing new algorithms, training models, and deploying models into production. This course may be useful for those interested in machine learning engineering, as it provides a foundation in image processing techniques that are used in many machine learning applications.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, such as operating systems, web applications, and mobile applications. This course may be useful for those interested in becoming a Software Engineer, as it provides a foundation in image processing techniques that are used in software development applications.
Web Developer
Web Developers design, develop, and maintain websites. They work on a variety of projects, such as e-commerce websites, social media websites, and corporate websites. This course may be useful for those interested in becoming a Web Developer, as it provides a foundation in image processing techniques that are used in web development applications.
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They work on a variety of robots, such as industrial robots, medical robots, and military robots. This course may be useful for those interested in becoming a Robotics Engineer, as it provides a foundation in image processing techniques that are used in robotics applications.

Reading list

We've selected ten 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 Building Image Processing Applications Using scikit-image.
Provides a comprehensive overview of digital image processing techniques, including image enhancement, feature extraction, and image segmentation. It valuable resource for both beginners and experienced image processing practitioners.
Provides a comprehensive introduction to computer vision algorithms and their applications. It covers a wide range of topics, including image formation, feature extraction, object recognition, and motion analysis.
Provides a comprehensive overview of digital image processing techniques using MATLAB. It valuable resource for both beginners and experienced image processing practitioners who use MATLAB.
Provides a comprehensive overview of image processing techniques for computer vision. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of advanced methods in computer vision and machine learning. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of computer vision models, learning, and inference. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of pattern classification techniques. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of deep learning for computer vision. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of generative adversarial networks. It valuable resource for both beginners and experienced computer vision practitioners.
Provides a comprehensive overview of image processing in Python. It valuable resource for both beginners and experienced computer vision practitioners who use Python.

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