Computer Vision 2016/2017
Official course page
http://www.unive.it/data/course/218005Moodle page (requires course enrollment)
https://moodle.unive.it/course/view.php?id=962Referral texts
- R. Szeliski. Computer Vision Algorithms and Applications. Springer - R. C. Gonzalez e R.E. Woods. Digital Image Processing (3rd edition). Pretience HallFinal 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, 6th 2017
- Assignment 2 Due date: March, 15th 2017
- Assignment 3 Due date: April, 30th 2017
Course slides:
- Introduction
- Image Formation Process
- Intensity transformations
- Color Vision
- Spatial Filtering
- Filtering in Frequency Domain
- Edge Features
- Morphological Image processing
- Finding Curves
- Point Features
- Geometric Primitives and Transformations
- The Pinhole Camera Model
Other materials:
Last Lesson:
> March 27st
Lab Session #4: The final project
Past Lessons:
> March 21st
The Pinhole camera model
- Camera obscura
- Cameras and Lenses
- Radial distortion
- The pinhole model
- Intrinsic and Extrinsic parameters
> March 20th
Lab session #3
- Geometric primitives
- The 2D Projective space
- Homogeneous coordinates
- Projectivities
- Image warping and interpolation
> March 14th
Point Features (part 2)
- Scale-invariant Feature Transform (SIFT)
> March 13th
Point Features (part 1)
- Harris corner detector
> March 7th
Finding curves
- Finding lines
- The RANSAC algorithm
- The Hough transform for lines
- The Hough transform for circles and other curves
> March 6th
Lab session #2: Morphological image processing
- Dilation and Erosion
- Opening and closing
- Boundary following
> February 28th
Edge features
- Features in computer vision
- Edge models
- Image gradient
- Derivatives and noise
- Marr-Hildreth edge detector
- Canny edge detector
> February 27th
Filtering in the frequency domain
- Continuous Fourier transform
- DFT
- Spectrum / Phase angle
- 2D Convolution theorem
- Low-pass, High-pass filters
- Notch filters
- Deconvolution
> February 21st
Spatial Filtering
- Linear filter
- Correlation and Convolution
- Template matching
- Smoothing
- Order-statistic filter
- Sharpening
> February 20th
Lab session #1
> February 14th
Color vision
- Color fundamentals
- Human vision
- Color matching
- Color models
- Color cameras
- Transformations
- Chroma keying compositing
> February 13th
Intensity transformations
- Negative
- Gain/bias
- Log/Gamma
- Image Histogram
- Otsu global thresholding
> February 7th
The image formation process
- Light and the visible spectrum
- The BRDF
- The imaging process
- Sampling and Quantization
- Relationships between pixels
> February 6th
Introduction
- Course informations
- What is computer vision?
- Optical illusions to understand human vision
- Computer vision vs. Computer graphics
- Applications
- A brief history