# Computer Vision: 3. Images

## 3.1 Image
### Resizing image
- if we throw away even rows and columns -> losing informations

### Camera image as a function
- f(x,y) : position to light
- cameras cannot measure continuous space

### Sub-sampling camera
- aliasing; dropping the rest of the rows and columns
 - CMOS sensor have this problem

### Signal to Noise Ratio (SNR)
- low light -> high SNR
- we should collect as much light as we can
 - bigger sensors, larger fill factor
 - minimize off time, long exposures (slower shutter speed)

## 3.2 Image sensors
### CMOS
- (+) random access to pixels
- (+) cheaper
- (-) lower fill factor

### CCD
- (+) larger fill factor
- (-) harder to read out image
- (-) more expensive

### Rolling shutter
- Camera sensors work like a scanner.
- Reading values from each sensor cannot happen simultaneously.
- Fast moving object will create an image different from our sight

## 3.3 Image sampling
### Aliasing
### Nyquist rate
- is a minimum sampling rate to avoid aliasing
 - >= two samples per cycle

### Fourier transform
- decomposes any signal into components
- we can figure out max frequency in components
- to avoid aliasing, samplint rate >= 2 * max frequency

### Low pass filtering (optical)
- allows the low frequencies to pass
- 

## 3.4 Image scaling
### Image as array of pixels
- F[x,y] : pixels
- f(x,y) : a continuous function

## 3.5 Filtering
- Mean filtering
 - mean value from nearby 8 & 1 itself

## Up-resizing

## 3.6 Aliasing
## 3.7 Format
- Compression ways
 - Lossless compression
 - Lossy compression
- Formats
 - JPEG
 - GIF
 - PNG