processamento de imagem e visão

 

 

 

 

 

 

Instructors:

Jorge Salvador Marques

Lectures

email: jsm at isr.ist.utl.pt

João Paulo Costeira

Lab

email: jpc at isr.ist.utl.pt

 

Office hours:

JSM: Monday 15:30-17:00 and Friday 14:00-15:30

Please send an email the day before until 18:00.

Goal

This course addresses the following question: 

           how to extract meaningful information from images and video?

This is a key question in many contexts, for example, when we wish to make robot see, when we wish to reconstruct 3D models of the scene from multiple images, when we want to search for similar images in a large database or when we wish to perform the analysis of medical images to detect abnormal issues.

Some of these problems are discussed in the course e.g., object segmentation, object recognition and 3D reconstruction. The course also provides a hands on approach to computer vision by involving the students in a challenging project.

 

Text book and material

         [RS] - Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2011 (a pdf version is freely available at: http://szeliski.org/Book/)

          Slides: available at fenix web page [link]

         Camera pose estimation

         Exercises (draft version)

Each student should attend all lessons and spend 4 hours per week, on average, preparing the Lab and reading the textbook.

 

Other interesting material

         Computer Vision course (University of Edimburg, Prof. Bob Fisher) http://www.inf.ed.ac.uk/teaching/courses/av/

         SIFT slides https://www.cs.cmu.edu/~efros/courses/AP06/presentations/0319.SIFT.ppt

 

Syllabus

All the topics are addressed in Szeliski book with detail. However, we do not discuss all the models and methods described in each chapter but s subset of them.

 

Chapter RS

0. Introduction

Course presentation, project overview.

1

1. Geometry background

points, lines and planes, geometric transformations

2

2. Camera model

Pin hole model, geometric properties, image motion, extrinsic and intrinsic parameters, camera calibration

2

3. Image processing

Images, linear filtering, non-linear filtering, interpolation and pyramids.

3

4. Feature detection and matching

Interest points, patches, SIFT, edges and line features, texture and color

4

5. Global motion estimation

Feature based methods, dense methods

6

6. Optical flow

Optical flow equation, Horn-Schunk and Lucas-Kanade methods.

5

7. Segmentation

Thresholding, region growing, snakes, level sets.

5

8. Structure from motion

Triangulation, epipolar geometry, estimation of fundamental matrix.

7

9. Object Recognition

Face and pedestrian detection. Category recognition. Bag-of-words

14

 

Evaluation

Student grading is based on 2 written tests (50%) and a project (50%). Minimal requirements: 9.5 in each of them.

The project is done in groups of 3.

 

Important dates:

Tests:          28 Out and 12 Jan

Exam (rec): 28 Jan