# F06 Images Graphics Vision Poop

## Prerequisites

- Calculus of single and multiple variables
- Linear algebra: points & vectors, dot and cross products, orthogonality, linear transformations by matrix multiplication
- COMP 410: Data Structures

## Syllabus

- Images and objects (1.5 weeks)
- Forms of discrete representation
- Sampled
- Via composition of functions on parameters

- Noise
- Blurring and distortion
- Color and shading

- Forms of discrete representation
- Sensing (2.0 weeks)
- Cameras
- Light cameras: analog to film, analog electronic, digital, digitizers, cine, Pinhole, lens
- X-ray and ultrasound imagers
- Retinal sensors, preprocessing through LGN to V1
- Receptive fields, spatial scales
- Spatial and temporal derivatives

- Resolution in sensing
- Spatial distortion
- Sampling and integration, aliasing
- Form sensing in human vision, structure of the human visual system

- Cameras
- Display (2.25 weeks)
- Devices and their operation: CRT & video, intensity & color, color spaces
- Display as composition of intensity transformation, display device, & perception
- Intensity & color perception
- Display linearization: gamma correction, just noticeable differences
- Contrast & brightness control: intensity windowing, histogram equalization, lookup tables
- Drawing lines and text
- Object rendering
- Normals & shading
- Reflectivity & illumination: ambient, diffuse, specular reflections

- Noise, unwanted details, & scale choice in both images and objects (4.0 weeks)
- Scale: level of detail, aperture, interrelation distances
- Apertures
- Global
- Orthogonal function compositions: Fourier, spherical harmonics
- Filtering
- Speedy Fourier analysis: the DFT and the FFT
- Singular Value Decomposition & Principal Component Analysis

- Local
- Gaussian and its derivatives
- Wavelets
- Splines

- Global

- Apertures

- Scale: level of detail, aperture, interrelation distances
- Spatial/time sampling interpolation from discrete to continuous (2.0 weeks)
- Smoothing
- Weakening small levels of detail
- Coarser sampling

- Sampling rate choice and anti-aliasing
- Multiscale sampling
- Interpolation, as convolution

- Smoothing
- Selected 3D matters (2.25 weeks)
- Similarity transformations in 3D
- By 3D matrices plus vector addition, incl. rotation through Euler angles
- Rotation via quaternions
- Orthogonal projection, change of coordinate systems

- Homogeneous coordinates & transformation matrices
- 2D images from 3D
- Perspective & perspective projection
- X-ray (accumulated projection)

- Similarity transformations in 3D

## Texts

Computer Graphics: Principles and Practice in C by James D. Foley, Andries van Dam, Steven K. Feiner and John F. Hughes.

## Recommended Computing environment

MATLAB. Recommended reference: Mastering MATLAB 7 by Duane C. Hanselman and Bruce L. Littlefield.

If you are bold, brave, and open source oriented, you can do everything you need to do in SciPy.

## Honor Code

In all course activities you must give credit where credit is due.

## Course Work and Grading.

- Homework (biweekly)
- On time is the beginning of class on the day due.
- Slightly late is before grading for that assignment is complete
- Very late is after that

- Midterm exam
- Final exam

`H_o` = [ on time homework grades ]

`H_s` = [ slightly late homework grades ]

`H_v` = [ very late homework grades ]

`N` = number of homeworks

`M` = midterm grade

`F` = final exam grade

With all grades normalized to 0 – 100%

` Grade = 4/9 (sum H_o + 9/10 sum H_s + 6/10 sum H_v)/N + 5/9 (max(M,F) + F)/2 + Diddly `