Image Processing
Computer Engineering
9CFU

Fall 2015
Monday 11.30-1.30pm M11,
Tuesday 8.30-10.30am M11
Wednesday 8.30-11.30am M12



Cosimo Distante
Instructor
Pier Luigi Mazzeo
Lab lectures


The goal of Image Processing is to compute properties of the three-dimensional world from digital images. Problems in this field include identifying the 3D shape of an environment, determining how things are moving, and recognizing familiar people and objects, all through analysis of images and video. This course provides an introduction to Image Processing, including such topics as feature detection, image segmentation, motion estimation, image mosaics, 3D shape reconstruction, and object recognition.


Textbook
R. Szeliski, Computer Vision: Algorithms and Applications


Course Syllabus


Sept. 21 Introduction
Sept. 22 Lab introduction
Sept. 23
Slide Cameras Book chapter
Sept. 28-29
Slide_Monochrome_Color (color book chapter, additional paper)
Sept. 30 Lab
Oct. 5 Geometric primitives (Section 2.1 Szelinski’s book)
Oct. 6 Filters
Oct. 7 Lab
Oct 12
Filters
Oct 14 Lab
Oct. 21

Oct. 26 Local Features
Oct. 27 Local Features
Oct. 28 Local Features
Nov 2
Alignment
Nov 3 Ransac (
further Readings)
Nov 4 Lab
Nov 11
Segmentation (further readings clustering)
Nov 16 Hough Transform
Foreground-Background
Nov 17
Optic Flow - Horn & Schunck
Nov 18 LAB
Nov 23 Optic Flow Lucas-Kanade (Matlab Files
HS.zip, LucasKanade.zip)
Nov 24
Texture Analysis (LBP) (further readings)
Nov 25 LAB
Nov 30 LAB
Dec 1 LAB
Dec 2
HOG
Dec 4 GLRM Gray level Co-occurence matrix,
Slide
Stereo Vision (further readings on multiple view geometry)
Dec 9
Stereo Vision further readings
Dec 14 Eigenface
slide (further readings)

Further Readings

* Gonzalez and Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2008 ISBN: 9780131687288
* R. O. Duda, P. E. Hart e D. G. Stork. Pattern Classification. Seconda Edizione, New York: John Wiley Interscience, 2001.
* David G. Stork, Elad Yom-Tov, Computer Manual in MATLAB to accompany Pattern Classification, Wiley Interscience ISBN: 0-471-42977-5
* Simon Haykin, Neural Netoworks A comprehensive foundation, Second Ed. Prentice Hall 1999