Image Processing, Features & Segmentation from Coursera

This course empowers learners to develop image processing programs and leverage MATLAB functionalities to implement sophisticated image applications. It provides a rich explanation of the fundamentals of computer visionâ€™s lower- and mid-level tasks by examining several principle approaches and their historical roots. By the end of the course, learners are prepared to analyze images in frequency domain. Topics include image filters, image features and matching, and image segmentation. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the second course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks.

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Examines the principles of image processing using MATLAB, which is a fundamental tool in the field of computer sciences

Develops foundational skills and knowledge necessary for creating sophisticated vision applications

Enhances understanding of the fundamentals of image processing

Provides a blend of theory and hands-on practice through online labs

Introduces concepts and techniques relevant to image analysis and understanding

Requires learners to have basic programming skills, specifically in MATLAB

Prerequisites exist for this course

Reviews summary

Introductory computer vision course

This course teaches the basics of computer vision. Students new to the subject may find the learning curve to be steep. However, the course covers core principles and several practical applications. Students completing this course will be prepared to analyze images in frequency domain and gain the foundational knowledge necessary to progress towards designing sophisticated vision applications.

Covers up-to-date and relevant computer vision topics.

"Concept was really good and trendy."

Covers a range of practical computer vision applications.

"However its not reached my expectation , but course made us go through various important image processing aspects like edge detection, corner detection and segmentation."

Focuses on basic concepts without delving into details of algorithms.

"Only basic concept were explained in superficial level and not the exact algorithm which are actually essential for graded exercises."

Study materials and videos are not well-organized.

"But videos and study materials are messy."

Heavy reliance on discussion forums for learning.

"Overall is a very steep learning curve, mostly on discussion among students to figure out the solutions of the assignments..."

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 Processing, Features & Segmentation with these activities:

MATLAB for Image Processing

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This will help you quickly become proficient in utilizing MATLAB for image processing tasks, which will be essential for completing assignments in this course.

Browse courses on MATLAB

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Complete several guided tutorials on MATLAB's image processing toolbox.
Practice writing simple MATLAB scripts for image processing tasks.

Basics of Image Processing

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This will familiarize you with the fundamental concepts and methodologies in image processing and will prepare you to complete further activities in this course.

Browse courses on Image Processing

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Review digital image representation, sampling and quantization.
Understand and implement basic image processing algorithms (e.g., smoothing, sharpening).

Image Processing Resources

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This activity will be a valuable resource for you throughout the course, providing easy access to essential materials.

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Create a collection of useful links to image processing tutorials, articles, and datasets.
Organize the resources logically and make them easily accessible.

Three other activities

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Image Analysis Discussion Forum

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This activity will provide you with the opportunity to engage with your peers, discuss image processing concepts, and get feedback on your work.

Browse courses on Image Analysis

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Participate in online discussions on image analysis topics.
Share your insights and ask questions to enhance your understanding.

Implement Image Filters

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This activity will enable you to design and apply a variety of image filters to real-world images.

Browse courses on Image Filters

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Choose an image and apply a variety of filters to it.
Analyze the results and explain how each filter affects the image.

Image Processing Coding Exercises

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This activity will solidify your understanding of image processing algorithms and techniques by providing you with hands-on practice.

Browse courses on Image Processing

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Solve a series of coding exercises on image processing.
Implement solutions to image processing problems.

Career center

Learners who complete Image Processing, Features & Segmentation will develop knowledge and skills that may be useful to these careers:

Image Processing Engineer

An Image Processing Engineer designs, develops, and maintains image processing systems. Image Processing, Features & Segmentation may be extremely useful in this role, as it provides a foundation in the fundamental concepts of image processing.

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Computer Vision Engineer

A Computer Vision Engineer designs, develops, tests, and maintains computer vision systems. Image Processing, Features & Segmentation may be useful in this role, which combines advanced image processing with machine learning to perform tasks that mimic human vision.

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Deep Learning Engineer

A Deep Learning Engineer designs, develops, and maintains deep learning models. Image Processing, Features & Segmentation may be useful in this role, as deep learning models are often used for image analysis and recognition.

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Machine Learning Engineer

A Machine Learning Engineer designs, develops, and maintains machine learning models. Image Processing, Features & Segmentation may be helpful in this role, as machine learning models are often used for image analysis and recognition.

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Algorithm Developer

An Algorithm Developer researches, designs, develops, and implements algorithms to improve efficiency, productivity, and performance. Image Processing, Features & Segmentation may help build a necessary foundation, considering that Algorithm Developers write mathematical equations to create algorithms, many of which rely on visual learning. This may involve image processing.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. Image Processing, Features & Segmentation may be useful in this role, as it provides a foundation in the fundamental concepts of image processing.

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Product Manager

A Product Manager researches, designs, and develops products. Image Processing, Features & Segmentation may be useful in this role, as many products incorporate image processing features.

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User Experience (UX) Designer

A User Experience (UX) Designer designs and evaluates the user experience of products and services. Image Processing, Features & Segmentation may be helpful in this role, given that UX designers often work with image processing features.

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Systems Engineer

A Systems Engineer designs, develops, and maintains complex systems. Image Processing, Features & Segmentation may be helpful in this role, given that systems engineers often work with image processing systems.

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Technical Architect

A Technical Architect designs and develops the architecture of software systems. Image Processing, Features & Segmentation may be useful in this role, given that technical architects often work with image processing systems.

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Data Scientist

A Data Scientist designs and implements statistical models and algorithms to extract insights from data. An understanding of image processing, as taught in Image Processing, Features & Segmentation, may be helpful for leveraging image data.

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Research Scientist

A Research Scientist conducts research in a specific field. Image Processing, Features & Segmentation may be useful in this role, as it provides a foundation in the fundamental concepts of image processing.

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Software Development Manager

A Software Development Manager plans, coordinates, and oversees the development of software products. Image Processing, Features & Segmentation may be helpful in this role, as it provides a foundation in the fundamental concepts of image processing.

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Data Analyst

A Data Analyst collects, transforms, and analyzes data to identify trends and patterns. Image Processing, Features & Segmentation may help build a necessary foundation for this role, given that Data Analysts often use image processing to extract meaningful insights from images.

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Statistician

A Statistician collects, analyzes, interprets, and presents data. Image Processing, Features & Segmentation may be helpful in this role, as statisticians often use image processing to extract meaningful insights from images.

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