Explore image transformation techniques in this MIT Computational Thinking lecture from the Spring 2021 series. Dive into topics such as downsampling, upsampling, linear combinations, element-wise multiplication, convex combinations, and image filtering using convolutions. Learn about the Unitful.jl package for adding units to numbers in Julia, and gain insights into computer science concepts like complexity, GPU architectures, and data structures. Discover practical applications through Photoshop-like manipulations and examine various kernels, including the Gaussian filter. Conclude with a discussion on discrete vs. continuous representations and boundary handling in image processing.
Transforming Images and Image Processing Techniques - Lecture 2
The Julia Programming Language via YouTube
Overview
Syllabus
Welcome.
Announcement: Lectures will be nearly an hour.
Unitful.jl package: adding units to numbers just works in Julia.
Transforming images.
Downsampling/Upsampling.
Linear Combinations (Combining images).
Element-wise multiplication (broadcast).
Convex Combinations.
Fun with Photoshop.
Image Filtering (convolutions).
Definition of convolutions and kernels.
Computer Science: Complexity.
Computer Science: Architectures, GPUs or Graphical Processing Units.
Playing with a few kernels.
Gaussian Filter.
Computer Science: Data Structure: Offset Arrays.
Discrete vs Continuous.
Respect my Boundaries.
Taught by
The Julia Programming Language
