# Lecture 1

## Introducing the syllabus

See the syllabus on Canvas.

## Motivational introduction for computer vision

Computer vision is the study of manipulating images.

Automatic understanding of images and videos

1. vision for measurement (measurement, segmentation)
2. vision for perception, interpretation (labeling)
3. search and organization (retrieval, image or video archives)

### What is image

A 2d array of numbers.

### Vision is hard

connection to graphics.

computer vision need to generate the model from the image.

#### Are A and B the same color?

It depends on the context what you mean by "the same".

todo

#### Chair detector example.

double for loops.

#### Our visual system is not perfect.

Some optical illusion images.

todo, embed images here.

### Ridiculously brief history of computer vision

1960s: interpretation of synthetic worlds
1970s: some progress on interpreting selected images
1980s: ANNs come and go; shift toward geometry and increased mathematical rigor
1990s: face recognition; statistical analysis in vogue
2000s: becoming useful; significant use of machine learning; large annotated datasets available; video processing starts.
2010s: Deep learning with ConvNets
2020s: String synthesis; continued improvement across tasks, vision-language models.

## How computer vision is used now

### OCR, Optical Character Recognition

Technology to convert scanned docs to text.


