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Process Images, Create Captioning AI Models

This course is part of multiple programs.

Instructor: Hurix Digital

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Image preprocessing using normalization and color-space conversion ensures stable training and consistent model performance.
Optical flow and frame differencing complement motion analysis, helping systems capture scene dynamics over time.
Preprocessing is essential for vision tasks, directly affecting model convergence, stability, and real-world results
Motion feature extraction links static images with dynamic understanding for recognition, tracking, and navigation.

Skills you'll gain

Tools you'll learn

Details to know

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Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

Master the essential preprocessing techniques that transform raw visual data into model-ready inputs for computer vision systems. This course empowers you to systematically prepare image data through normalization and color-space conversions, then advance to extracting meaningful motion information from video sequences. You'll apply pixel value normalization, execute color transformations between RGB, grayscale, HSV, and BGR formats, then implement optical flow algorithms and frame differencing to capture temporal dynamics. By completing this course, you'll be able to:

• Apply normalization and color-space conversions to preprocess image data • Apply optical flow and frame differencing techniques to extract motion features from video This course is unique because it combines fundamental preprocessing with advanced motion analysis in practical, hands-on implementations. To be successful in this project, you should have a background in Python programming, basic computer vision concepts, and familiarity with NumPy arrays.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.

Module details

Learners will master systematic image preprocessing techniques including normalization and color-space conversions to prepare raw visual data for computer vision applications.

What's included

3 videos1 reading1 assignment1 ungraded lab

3 videosTotal 17 minutes

Why Image Preprocessing Matters in Computer Vision3 minutes
Implementing Normalization Techniques with NumPy7 minutes
Converting Between Color Spaces with OpenCV7 minutes

1 readingTotal 10 minutes

Fundamentals of Image Normalization and Color Space Theory10 minutes

1 assignmentTotal 8 minutes

Image Preprocessing Fundamentals Assessment8 minutes

1 ungraded labTotal 18 minutes

Image Preprocessing Pipeline: Normalization & Color-Space Transformations18 minutes

Learners will master optical flow and frame differencing techniques to extract temporal motion features from video sequences for computer vision applications.