Computer Vision 2018/2019
Official course page
https://www.unive.it/data/course/255294Moodle 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
- D. Forsyth, J. Ponce. Computer Vision: A Modern Approach (2nd edition). Pearson
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
- Flow and tracking
- 2D Projective geometry
- 3D Projective geometry
- Pinhole camera
- Camera calibration
- Epipolar geometry
Lab slides:
Final Project:
Download the document 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
Other materials:
- Lab session #1 notebook and data: chroma.zip
- FFT and template matching examples: FFT_and_templatematch.zip
- Deblurring example: deblur.zip
> Lesson 24: April 30th
Laboratory
- Students' seminars
> Lesson 23: April 29th
Laboratory
- Chessboard detection for camera calibration
> Lesson 22: April 24th
Final project
- Discussion about the exam
> Lesson 21: April 23rd
Epipolar geometry
- The Fundamental matrix: geometric and algebraic derivation
- F related to K, R and T
- Properties of the Fundamental matrix
- F produced by a pure translation
- The Essential matrix
- The 8-points algorithm
- Triangulation
> Lesson 20: April 16th
Camera Calibration
- Camera pose recovery
- The final non-linear optimization
- Parametrizing R with the Rodrigues formula
- Examples
Epipolar geometry
- Reconstruction ambiguity
- Corresponding points and the epipolar plane
> Lesson 19: April 15th
Camera Calibration
- Calibration targets
- Basic equations and the DLT solution
- Zhang's camera calibration
- Recovering K from Homographies
Pinhole camera model
- Radial distortion
> Lesson 18: April 9th
Pinhole camera model
- Camera obscura and the pinhole model
- Camera and lenses
- Equations of the pinhole camera model
- Intrinsic and extrinsic parameters
- The projection matrix
- Finite projective camera and its decomposition
- Image of points on a plane
- Homography estimation and error functions
- Vanishing points and lines
> Lesson 17: April 8th
2D Projective geometry and transformations
- Conics and dual conics
- Projectivities in the 2D projective space
- Projective vs. Affine transformation
- Spatial transformations on images: forward and inverse warp
- Nearest-neighbour and bilinear interpolation
3D projective geometry
- Projective 3-space
- 3D planes
- Rigid motions
- Projective transformation
- The plane at infinity
> Lesson 16: April 2nd
Flow and tracking
- Tracking as inference problem
- Linear Kalman Filter
- Traking with Kalman filter
2D Projective geometry and transformations
- The 2D projective space
- Points and lines
- The duality principle
- Ideal points and line at infinity
- The projective plane
> Lesson 15: April 1st
Flow and tracking
- Motion field and Optical flow
- Brightness constancy equation
- The aperture problem
- Lukas-Kanade flow
- Feature-based techniques
- Linear assignment problem
> Lesson 14: March 26th
Point Features
- SIFT
- Feature matching
> Lesson 13: March 25th
Point Features
- Harris corner detector
> Lesson 12: March 20th
Finding Curves
- The RANSAC algorithm
- The Hough transform for lines and circles
- Line fitting examples
> Lesson 11: March 19th
Finding Curves
- Problem overview
- Line fitting
- Implicit curves
- Distance functions and approximations
> Lesson 10: March 13th
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: March 4th
Filtering in frequency domain
- Low-pass and High-pass filters
- Notch filters
- Deconvolution
- Practical examples of some filtering techniques
> Lesson 7: February 25th
Spatial filtering
- Sharpening filters
- Image Laplacian
Filtering in frequency domain
- Continuous Fourier transform
- DFT
- Spectrum / Phase angle
- 2D Convolution theorem
> Lesson 6: February 19th
Spatial filtering
- Mechanics of spatial filtering
- Linear filters
- Correlation and convolution
- Template matching
- Smoothing spatial filters
- Min, max and median filters
- Filters and noise
> Lesson 5: February 18th
Laboratory session
- High vs Low level programming languages for scientific computing
- Python ecosystem
- Numpy library
- Loading images with OpenCV
- Image pixels' manipulation
> Lesson 4: February 12th
Color vision
- Color fundamentals
- Human vision
- Color matching
- Color models
- Color cameras
- Transformations
- Chroma keying compositing
> Lesson 3: February 11
Intensity transformations
- Negative
- Gain/bias
- Log/Gamma
- Image Histogram
- Histogram equalization and matching
> Lesson 2: February 5th
The image formation process
- Light and the visible spectrum
- The BRDF
- The imaging process
- Sampling and Quantization
- Relationships between pixels
> Lesson 1: February 4th
Introduction
- About this course
- What is computer vision?
- Optical illusions to understand human vision
- Computer vision vs. Computer graphics
- Computer vision applications