# Topic 23 – Introduction to Computer Vision

Why do I need to learn about computer vision?

Computer vision has become more and more interesting. Image recognition, autonomous driving, and disease detection are examples of breakthrough achievements in the field.

Nowadays a key skill that is often required from a software developer is the ability to use computer vision algorithms and tools to solve real-world problems related to images and videos.

What can I do after finishing learning about applied computer vision?

You will be to create software that could recognize recognize a face or transform a picture of young person to old person.

That sounds fun! What should I do now?

– audit these Deep Learning Specialization courses and
– read this Francois Chollet (2021). Deep Learning with Python. Manning Publications book, and
– this Michael A. Nielsen (2015). Neural Networks and Deep Learning. Determination Press book.

After that please read this David Foster (2023). Generative Deep Learning – Teaching Machines To Paint, Write, Compose, and Play. O’Reilly Media book.

After that please read this Ian Goodfellow et al. (2016). Deep Learning. The MIT Press book.

Terminology Review:

• Digital Image: f(x, y)
• Intensity (Gray Level): ℓ = f(x, y)
• Gray Scale: ℓ = 0 is considered black and ℓ = L – 1 is considered white.
• Quantization: Digitizing the amplitude values.
• Sampling: Digitizing the coordinate values.
• Representing Digital Images: Matrix or Vector.
• Pixel or Picture Element: An element of matrix or vector.
• Deep Learning.
• Artificial Neural Networks.
• Filter: 2-dimensional matrix commonly square in size containing weights shared all over the input space.
• The Convolution Operation: Element-wise multiply, and add the outputs.
• Stride: Filter step size.
• Upsampling: Nearest Neighbors, Linear Interpolation, Bilinear Interpolation.
• Max Pooling, Average Pooling, Min Pooling.
• Convolutional Layers.
• Feature Maps.
• Convolutional Neural Networks (CNNs).
• Object Detection.
• Face Recognition.
• YOLO Algorithm.
• Latent Variable.
• Autoencoders.
• Variational Autoencoders.
• Generators.
• Discriminators.
• Binary Cross Entropy Loss Function, Log Loss Function.