Introduction to Computer Vision from Coursera

In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene.

Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images.

You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work.

To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

What's inside

Syllabus

Introduction to Features

Working With Features

Image Registration

Image Stitching

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces computer vision concepts, algorithms, and applications relevant to engineering and science

Taught by recognized experts in the field of computer vision

Utilizes MATLAB, a widely used software in engineering and science

Suitable for learners with prior image processing experience

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 Introduction to Computer Vision with these activities:

Organize Course Resources

Show steps

Set yourself up for success by organizing and reviewing course materials.

Show steps

Download and save all course materials in a designated folder.
Create a system for organizing notes, assignments, and quizzes.
Review course materials regularly to reinforce your understanding.

Introduction to Feature Detection and Extraction

Show steps

Refresh your knowledge of feature detection and extraction to better prepare for the course.

Browse courses on Feature Detection

Show steps

Review the concepts of feature detection and extraction.
Practice identifying and extracting features from images using popular algorithms.
Explore different feature descriptors and their applications.

Image Registration Exercises

Show steps

Reinforce your understanding of image registration through repetitive exercises.

Browse courses on Image Registration

Show steps

Practice aligning images using different geometric transformations.
Evaluate the accuracy of image registration using quantitative metrics.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Applying Feature Matching to Image Stitching

Show steps

Enhance your understanding of feature matching by implementing image stitching techniques.

Browse courses on Image Stitching

Show steps

Follow tutorials on image stitching algorithms.
Implement feature matching and blending techniques to create stitched images.
Experiment with different image pairs and stitching parameters.

Visualize Feature Distributions

Show steps

Develop a deeper understanding of feature distributions by creating visualizations.

Show steps

Extract features from a dataset of images.
Use data visualization techniques to represent feature distributions.
Analyze the visualizations to identify patterns and insights.

Become a Mentor in the Course Discussion Forum

Show steps

Enhance your understanding of the course material and help fellow students by becoming a mentor in the discussion forum.

Show steps

Actively participate in the discussion forum and provide thoughtful responses.
Identify students who might benefit from your guidance.
Offer support and encouragement to your mentees.

Participate in a Computer Vision Hackathon

Show steps

Challenge yourself and gain practical experience by participating in a computer vision hackathon.

Show steps

Join a team or form your own for the hackathon.
Brainstorm and select a computer vision project idea.
Develop and implement a computer vision solution.
Present your project and compete for prizes.

Develop a Feature-Based Object Detection System

Show steps

Apply your knowledge and skills to build a feature-based object detection system as a capstone project.

Browse courses on Object Detection

Show steps

Design the architecture of the object detection system.
Implement the system using computer vision techniques and algorithms.
Evaluate the performance of the system on a dataset of images.
Document and present your project to the class.

Career center

Learners who complete Introduction to Computer Vision will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers design and develop systems that enable computers to see and interpret images and videos. This course provides a comprehensive overview of the fundamental concepts and algorithms used in Computer Vision, including feature detection, extraction, and matching. These techniques are essential for tasks such as object recognition, tracking, and image stitching, which are commonly used in applications such as self-driving cars, medical imaging, and robotics.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers build and deploy machine learning models to solve real-world problems. Image features are an important input for many machine learning algorithms, and this course provides a thorough understanding of how to extract and use features for image recognition, classification, and other tasks. This knowledge is essential for Machine Learning Engineers who want to develop high-performing models for computer vision applications.

See salaries and explore the career path for Machine Learning Engineer

Geospatial Analyst

Geospatial Analysts use geographic data to solve real-world problems. Image registration is an important technique for Geospatial Analysts who work with satellite imagery or other geospatial data that needs to be aligned. This course provides a practical introduction to the algorithms and techniques used for image registration, which can help Geospatial Analysts develop more accurate and robust models.

See salaries and explore the career path for Geospatial Analyst

Data Scientist

Data Scientists use mathematical and statistical methods to analyze data and extract insights. Image registration is an important technique for Data Scientists who work with geospatial data, medical imaging, or other fields where multiple images need to be aligned. This course provides a solid foundation in the algorithms and techniques used for image registration, which can help Data Scientists develop more accurate and robust models.

See salaries and explore the career path for Data Scientist

Robotics Engineer

Robotics Engineers design, build, and operate robots. Image registration is an important technique for Robotics Engineers who work with robots that need to navigate and interact with their environment. This course provides a solid foundation in the algorithms and techniques used for image registration, which can help Robotics Engineers develop more robust and autonomous robots.

See salaries and explore the career path for Robotics Engineer

Medical Image Analyst

Medical Image Analysts use imaging technologies to diagnose and treat medical conditions. Image stitching is an important technique for Medical Image Analysts who work with large medical images, such as MRI scans, that need to be combined into a single image. This course provides a practical introduction to the algorithms and techniques used for image stitching, which can help Medical Image Analysts develop more efficient and accurate workflows.

See salaries and explore the career path for Medical Image Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. Image processing is a common task in software development, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of software applications, including image editing software, medical imaging software, and computer vision applications.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to provide insights for decision-making. Image processing is a common task in data analysis, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of data analysis tasks, including image classification, object detection, and image segmentation.

See salaries and explore the career path for Data Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. Image processing is a common task in product development, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of products, including medical devices, consumer electronics, and industrial equipment.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers plan, execute, and close projects. Image processing is a common task in project management, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of project management tasks, including project planning, risk assessment, and project evaluation.

See salaries and explore the career path for Project Manager

Business Analyst

Business Analysts use data and analysis to improve business processes. Image processing is a common task in business analysis, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of business analysis tasks, including market research, customer segmentation, and process improvement.

See salaries and explore the career path for Business Analyst

Marketing Manager

Marketing Managers develop and execute marketing campaigns. Image processing is a common task in marketing, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of marketing tasks, including image editing, video production, and social media marketing.

See salaries and explore the career path for Marketing Manager

Technical Writer

Technical Writers create and maintain technical documentation. Image processing is a common task in technical writing, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of technical writing tasks, including user manuals, technical reports, and white papers.

See salaries and explore the career path for Technical Writer

Sales Engineer

Sales Engineers provide technical support to customers. Image processing is a common task in sales engineering, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of sales engineering tasks, including product demonstrations, customer training, and technical support.

See salaries and explore the career path for Sales Engineer

Quality Assurance Analyst

Quality Assurance Analysts test and evaluate software and systems to ensure they meet quality standards. Image processing is a common task in quality assurance, and this course provides a solid foundation in the algorithms and techniques used for image processing. This knowledge can be applied to a wide range of quality assurance tasks, including image inspection, defect detection, and image analysis.

See salaries and explore the career path for Quality Assurance Analyst

Introduction to Computer Vision

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses