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Objectives & Goals
Today, a plethora of image processing algorithms exists, ranging from very basic and easy to implement to highly sophisticated. At the same time, imaging sensors are becoming increasingly wide spread and are quickly being adopted by new application domains. To the practitioner it is often unclear which processes can be automated by image processing routines and which type of algorithm is best suited to solve the problem at hand. At the same time, image processing algorithms make implicit or explicit assumptions with respect to the data or the models used. Using image processing routines in a software package as a "black box" entails the risk to choose wrong parameters or or an algorithms in which some assumptions are violated. This may lead to inaccurate or downright wrong results.
In this short course, basic image processing tasks such as image enhancement / denoising, feature detection, object segmentation and -classification as well as motion estimation will be introduced. The concepts of the different approaches will be detailed. The focus will be on understanding how the algorithms work and which assumptions are made. We will skip over stringent mathematical proofs. This short course will introduce the basic algorithms but also give an overview of current state-of-the-art approaches. The course is aimed at practitioners from all fields, but the main examples will be from the field of biology, fluid mechanics and environmental sciences. The course will consist of lectures in the morning and hands on practicals in the afternoon. Basic programming skills are advantageous.
Course Materials
Lecture Slides:Excercises:
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