Computer Vision 2017/2018
Official course page
http://www.unive.it/data/course/230457Moodle page (requires course enrollment)
https://moodle.unive.it/course/view.php?id=962Weekly 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