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 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:

Other materials:



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. Geometric primitives and transformations
  12. 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



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