Filippo Bergamasco
PhD
Assistant Professor
Department of Environmental Sciences, Informatics and Statistics (DAIS)
Universtà Ca' Foscari of Venice
Via Torino, 155
Venezia Mestre 30172 - ITALY
Phone: +39 041 234 8418
Mail: filippo.bergamasco AT unive.it

Computer Vision 2018/2019

Official course page

https://www.unive.it/data/course/255294

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
  • D. Forsyth, J. Ponce. Computer Vision: A Modern Approach (2nd edition). Pearson


Course slides:

  1. Introduction
  2. Image Formation Process
  3. Intensity transformations
  4. Color vision
  5. Spatial filtering
  6. Filtering in frequency domain
  7. Morphological image processing
  8. Edge features
  9. Finding curves
  10. Point features
  11. Flow and tracking
  12. 2D Projective geometry
  13. 3D Projective geometry
  14. Pinhole camera
  15. Camera calibration
  16. Epipolar geometry

Lab slides:

  1. Python / numpy / OpenCV

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:

> 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



Last update Tue Feb 08 2022 09:22:04 GMT+0000 (Coordinated Universal Time)