On this page, you should find all the information about exercises of the Image Analysis course.

Evaluation - how can you earn a credit

During the exercises, we will cover some topic of digital image processing. Your task is complete and handover them for final grading. We'll use the OpenCV library in our implementation.

Exercise 1

We'll implement thresholding of objects.

We'll use the image below for our experiments:
Train Image

Text describing the content of the exercise: features_01.

Use this project to implement your codes.

Exercise 2

We'll implement object extraction, indexing, and simple features computation.

Text describing the content of the exercise: features_01.

Exercise 3

We'll implement features computation: features_02

Exercise 4

We'll implement saving the ethalons features and use this features to classify objects in a new image. A description is provided in this document.

Exercise 5

We'll implemente k-means algorithm for simple clustering of extracted features.

Exercise 6

We'll use a back propagation neural network as a classifier. See text about back propagation neral network for details. The basic structure of a back propagation neural network is in the following file: bpnn.zip

Exercise 7

We'll use a back propagation neural network as a classifier.

Exercise 8

We'll look at the motion detection based on background modeling usign gaussion mixture model. The text that describes the method is available here: mog.pdf (in Czech, author: Tomáš Fabián)

A nice introduction into motion detection in English: Understading Background Mixture Models for Foreground Segmentation.

A video that we can use for our experiments is also available: dt_passat.mpg

Exercise 10

We'll implement the Histogram of Oriented Gradients for object classification/detection.

Image for HOG