1- Rapid advances in computer vision is enabling brand new applications the were not possible a few years ago.

2- Since the community is creative, it can inspire applications in other areas.

University of Michigan - Ann Arbor

Computer Vision

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Why computer vision is interesting?

What is the problem of using deep learning on images?

1- Image classification

2- Image localization

3- Object detection

4- Neural style transfer

Types of Computer Vision Problems

Acquired data is usually messy and can come from different sources, angles, color scales, etc. In order to use machine and deep learning techniques they need to be standardized. Usually the goal is to increase the accuracy while reducing the complexity of the model used. There is no single answer to preprocessing but there are a group of techniques commonly chosen from.

Computer Vision Pre-Processing

In computer vision there is many established datasets used for training and benchmarking different algorithms. Unlike other machine learning areas, computer vision datasets are much more standardized because it takes significantly more effort to create them.

Computer Vision Datasets

Computer vision has tons of applications but the methods used have become relatively standardized so it's often useful to use established libraries/tools. Some examples are:
- OpenCV
- Scipy ndimage
- TensorFlow
- PyTorch Vision
- MATLAB Computer Vision Toolbox

Computer Vision Libraries

- Human computer interaction (HCI)
- Interaction design
- Collaborative and social computing
- Ubiquitous and mobile computing
- Data Visualization
- Accessibility

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