Image Processing
Computer Engineering

Spring 2017
Monday 3.00pm-6.00pm I7,
Tuesday 11.30am-1.30pm M9,
Thursday 11.30am-1.30pm M9,

Cosimo Distante
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.


Course Syllabus

Mar. 2 Introduction
Mar. 6-7
Slide Cameras Book chapter
Mar. 16 Lab introduction
Mar. 20
Geometric primitives (Section 2.1 Szelinski’s book)
Mar. 21
Mar. 23 Laboratory
Mar. 27
Slide_Monochrome_Color (color book chapter, additional paper)
Mar. 28
Apr. 3
Apr. 4 Lab
Apr. 6
Image Processing in frequency domain (Fourier) (further readings on Gonzalez and Woods book and Lab’s Exercise Book)
Apr. 10
Pyramids and Blending (further readings on Pyramid’s original paper, Poisson Image Editing)
Apr. 11 Local Feature detectors
Apr. 17
Local Feature part 1
Apr. 18
Local Features part 2
Apr. 20 Lab
Apr. 24
PPT version Alignment Slides chapter 6 Szelinski (further readings DLT, PnP also in Szelinski par. 6.2)
Apr Lab
May 2,
Alignment Part2, further readings on DLT (Matlab Code: DLT and Non linear pose estimation)
May 4, Lab
May 8
May 9
May 11 Lab
May 19, 22, 23 Calibration and Stereo Vision (
slides) (further readings on Fundamental Matrix) Simple matlab code for disparity estimation

May 25, 29, 30 and June 1st
Lecture on Deep Learning

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