Time only SS
Date Wednesday, 08.15h-11.30h
Room 041 first, but strongly varying
Credits 4 SWS / 5 ECTS
Exam Presentation + oral


  • First lesson of term SS 24: 20.03.2024

Object Recognition

The goal of computer vision is to enable machines to see and understand data from images and videos. To achieve this goal the central computer vision task is object recognition. Due to the immense increase of image and video data, provided by digital cameras and made available in the internet, intelligent systems to monitor, find, filter and automatically organize visual data are urgently needed. In recent years, Deep Learning has revolutionized object recognition applications.

Besides seeing and understanding, recently the task of generating images and videos has become more and more popular - Dall-E and Stable-Diffusion are only two examples of this category.

This lecture provides a comprehensive insight into state of the art algorithms for object recognition and image generation. Well established methods for image-processing, filtering, feature-extraction and machine-learning are covered as well as the most recent and performant Deep Learning architectures. An overview is given in the image below:

In order to provide a better picture of visual object recognition only a few applications are listed here:

  • Face recognition
  • Driver assistance systems and autonomous driving
  • Optical inspection
  • Video surveillance systems, Tracking
  • Document forgery detection
  • Content-based image search (CBIR), automatic image clustering (Photo smartphone apps)
  • Video-data mining
  • Automatic image annotation and captioning
  • Background subtraction
  • Autostitching to create panorama views (several apps in the app stores)
  • Vision based interfaces, e.g. Kinect
  • Medical- and Neuroimaging, e.g. cancer detection
  • Pose Estimation
  • Style Transfer
  • Automatic Image Generation and Modification
  • Super Resolution

Lecture Contents

Today Machine Learning constitutes an essential part in Object Recognition. Therefore, it is best if you attend the Machine Learning lecture before the Object Recognition lecture. If this is not possible, you may self-study the basics of Machine Learning, Neural Networks and Deeplearning by these videos: