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IMAGE ANALYSIS AND COMPUTER VISION




INTRODUCTION

On this page, you will find all relevant information regarding your Image
Analysis and Computer Vision course, taught by Prof. Ender Konukoglu and Prof.
Fisher Yu.

CONTENT

This course aims at offering a self-contained account of computer vision and its
underlying concepts, including the recent use of deep learning. The first part
starts with an overview of existing and emerging applications that need computer
vision. It shows that the realm of image processing is no longer restricted to
the factory floor, but is entering several fields of our daily life. First the
interaction of light with matter is considered. The most important hardware
components such as cameras and illumination sources are also discussed. The
course then turns to image discretization, necessary to process images by
computer. The next part describes necessary pre-processing steps, that enhance
image quality and/or detect specific features. Linear and non-linear filters are
introduced for that purpose. The course will continue by analyzing procedures
allowing to extract additional types of basic information from multiple images,
with motion and 3D shape as two important examples. Finally, approaches for the
recognition of specific objects as well as object classes will be discussed and
analyzed. A major part at the end is devoted to deep learning and AI-based
approaches to image analysis. Its main focus is on object recognition, but also
other examples of image processing using deep neural nets are given.

OBJECTIVE

Overview of the most important concepts of image formation, perception and
analysis, and Computer Vision. Gaining own experience through theoretical and
programming exercises.

PREREQUISITES

Basic concepts of mathematical analysis and linear algebra. The programming part
of the exercises is based on Python and Linux. The course language is English.

LECTURES

The lectures will proceed with live attendence every Thursday, between
14:15-17:00, at HG F1.

EXERCISES

The course will include 6 programming based exercises. In order to be eligible
to answer the final exam, you will need to pass at least 3 of the 6 exercises.
Additionlly, you will get a +0.25 bonus grade if you finish all 6 exercises. The
course will also have lab sessions every Thursday, between 17:15-19:00, at ETZ
D61. The teaching assistants will be available during the lab sessions in case
of difficulties related to the exercises. Attendence to the lab sessions is
totally optional. More details about the exercises will be announced later.

PERFORMANCE ASSESSMENT

Different parts of the lecture will be assessed in a maximum 2 hours written
exam in English. Doctoral students who participate at the course to earn ECTS
points will receive a “Testat” without taking the written examination if their
department rules allow this and provided they successfully complete all 6
exercises. All other students must take the written examination.


LECTURE SCHEDULE

Here is a rough lecture schedule for the semester (may be updated during the
course of the semester):

Week #

Date

Title

1

22.09.2022

Introduction and Digital Image Formation

2

29.09.2022

Sampling and Enhancement

3

06.10.2022

Feature Extraction and PCA

4

13.10.2022

Color and Texture

5

20.10.2022

Traditional Object Recognition and Segmentation

6

27.10.2022

3D

7

03.11.2022

Tracking and Optical Flow

9

10.11.2022

Deep Learning - Basics

10

17.11.2022

Deep Learning - CNN

11

24.11.2022

Deep Learning - Recognition

12

01.12.2022

Deep Learning - Segmentation

13

08.12.2022

Deep Learning - Regression

14

15.12.2022

Unsupervised Learning

14

22.12.2022

Advanced Topics and Challenges



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