# Computer Vision 2017/2018

### Official course page

http://www.unive.it/data/course/230457### Moodle page (requires course enrollment)

https://moodle.unive.it/course/view.php?id=962### Weekly timetable:

Monday 12:15 - 13:45 (Aula Delta 2C)Tuesday 12:15 - 13:45 (Aula Delta 2C)

### Referral texts

- R. Szeliski. Computer Vision Algorithms and Applications. Springer
- R. C. Gonzalez e R.E. Woods. Digital Image Processing (3rd edition). Pretience Hall

### Final Project:

Download the package with all the information needed to develop your final project for the exam. Follow the instructions inside.

*NOTE: Submission of the final project must be performed via* moodle

### Assignments:

- Assignment 1 Due date: March, 5th 2018
- Assignment 2 Due date: March, 19th 2018
- Assignment 3 Due date: April, 16th 2018

### Other materials:

- Lab Companion Guide
- Deblurring example
- Template matching example
- OpenCV histogram
- OpenCV homography estimation

### Course slides:

- Introduction
- Image Formation Process
- Intensity transformations
- Color Vision
- Spatial Filtering
- Filtering in Frequency Domain
- Morphological image processing
- Edge features
- Finding Curves
- Point Features
- Geometric primitives and transformations
- The Pinhole camera model

### Last Lesson:

## > Lesson 15: March 26th

## The pinhole camera model

- Camera obscura and pinhole model
- Lenses
- Projections
- Intrinsic and extrinsic parameters

### Previous lessons:

## > Lesson 14: March 20th

## Geometric primitives and transformations

- The 2D projective space
- Points and lines
- Conics
- 2D projectivities
- Spatial transformations

## > Lesson 13: March 19th

## Point Features (Part 2)

- SIFT features

## > Lesson 12: March 13th

## Point Features (Part 1)

- Harris corner detector

## > Lesson 11: March 12th

## Finding curves

- Finding lines
- The RANSAC algorithm
- Parameter space
- The Hough transform for lines

## > Lesson 10: March 6th

## Edge features

- Features in computer vision
- Edge models
- Image gradient
- Derivatives and noise
- Marr-Hildreth edge detector
- Canny edge detector

## > Lesson 9: March 5th

## Morphological image processing

- Dilation and Erosion
- Opening and closing
- Boundary following
- Grayscale morphology

## > Lesson 8: February 27th

## Filtering in frequency domain

- Practical examples of some filtering techniques
- Notch filters
- Deconvolution

## > Lesson 7: February 26th

## Filtering in frequency domain

- Continuous Fourier transform
- DFT
- Spectrum / Phase angle
- 2D Convolution theorem
- Low-pass, High-pass filters

## > Lesson 6: February 20th

## Spatial filtering

- Mechanics of spatial filtering
- Linear filters
- Correlation and convolution
- Template matching
- Smoothing spatial filters
- Min, max and median filters
- Filters and noise
- Sharpening filters
- Image laplacian

## > Lesson 5: February 19th

## Laboratory (1st session) @ Lab3 (Building Z)

- The OpenCV library: building and basic principles
- Loading, visualizing and saving images
- Pixel manipulation
- Opening a webcam stream

## > Lesson 4: February 13th

## Color vision

- Color fundamentals
- Human vision
- Color matching
- Color models
- Color cameras
- Transformations
- Chroma keying compositing

## > Lesson 3: February 12th

## Intensity transformations

- Negative
- Gain/bias
- Log/Gamma
- Image Histogram
- Otsu global thresholding

## > Lesson 2: February 6th

## The image formation process

- Light and the visible spectrum
- The BRDF
- The imaging process
- Sampling and Quantization
- Relationships between pixels

## > Lesson 1: February 5th

## Introduction

- Course informations
- What is computer vision?
- Optical illusions to understand human vision
- Computer vision vs. Computer graphics
- Computer vision applications