Object recognition is a natural process of the human brain performed in the visual cortex. It is dependent on a binocular depth perception system that renders a three-dimensional representation of the objects in a scene. Hitherto, computer and software systems are been used to simulate the perception of three-dimensional environments with the aid of sensors to capture real-time images. In the process, such images are used as input data for further analysis and development of algorithms, an essential ingredient for simulating the complexity of human vision, so as to achieve scene interpretation for object recognition, similar to the way the human brain perceives it.

The rapid pace of technological advancements in hardware and software, are continuously bringing the machine-based process for object recognition nearer to the inhuman vision prototype. The key in this field, is the development of algorithms in order to achieve robust scene interpretation. A lot of recognizable and significant effort has been successfully carried out over the years in 2D object recognition, as opposed to 3D.

Significant research still needs to be done towards the enhancement of 3D object recognition in order to achieve a better interpretation and understanding of reality and the relationship between objects in a scene.

My goal is to continue my research in this field and to show how RGB and depth information can be utilized in order to develop a new class of 3D object recognition algorithms, analogous to the perception processed by the human brain.


  • RGB/-D Object Detection
  • RGB/-D Segmentation
  • Probabilistic Graphical Models
  • 3D Reconstruction
  • Bundle Adjustment

Research Projects

  • Leave a trace (2013)

    Tracking of People in an indoor environment

    Together with Prof. Tyyne Claudia Pollmann, of the “Kunsthochschule Berlin Weißensee”, we implemented a person tracking project. She had won the call for an exhibit within the new Charité building “Charité Cross Over”. The Team around Ralf Reulke was charged with the development of the system. A 2 Megapixel high quality camera was used with an 4 core computing unit and a very fast graphics card. The tracking software from a former project was adapted, such that heavy image computations were implemented in the graphical processing unit. The results showed a high frame rate processing of 12 to 13 frames per second.

    Team: Dominik Rueß, Kristian Manthey, Michele Adduci, Konstantinos Amplianitis

    leave a trace

    Reference Link

  • Securail (2012)

    Development of a Solution to Online Videosurveillance in Civil Railway Transportation Systems

    The aim of the project was to develop an optical system for the realization of monitoring functions based on the analysis of three-dimensional point clouds and derived quantities. The application is the rail-based public transport, the entire passenger compartment is to be recorded and analyzed. There are methods for fusing data to develop for noise removal and pattern recognition. To create it data basis two different camera systems (stereo and RGB-D) are used, stereo camera Hella People Counter and RGB-D Microsoft Kinect. People are considered 3D ellipsoids and their movement patterns analyzed. The multi-camera system using this poses particular challenges of synchronization and stability.


    Michele Adduci, Konstantinos Amplianitis, Martin Misgaiski-Hass, Christian Kaptur,Sourabh Bodas, Silvio Tristram


    Reference Link

  • Cassandra (2012)

    OpenCV wrapping for Cassandra Software

    The company Hella Aglaia Mobile Vision GmbH develops and distributes intelligent visual sensor systems. This also includes the software Cassandra, a tool for fast development of (prototypic) image processing algorithms. Cassandra is implemented in C++ and specializes on the automotive area. Cassandra has changed significantly for version 11: improved multi core support, better synchronization of data sources and sinks, realtime requirements can now be applied, since the system won’t be cluttered by too much data and many more features have been implemented. Cassandra will also be released in a community edition. This edition allows academic associates and students to rapidly develop and test their own image processing algorithms. The outstanding feature of Cassandra is the lack of need for programming skills. But still, if you know C++, you can extend Cassandra with your own stations.

    With the freely available third party library OpenCV users have a very powerful image processing tool. This library will be implemented in Cassandra 11. The task of the CV group is to reasonably implement OpenCV functions and classes to Cassandra stations. The problem is the different concept of OpenCV and Cassandra. The latter one makes use of data flow graphs. In contrast, OpenCV requires much more user input and control about parameters and the use of the classes.
    The OpenCV modul core has already been ported to Cassandra by Hella Aglaia. The CV group has implemented the modules calib3d, features2d, video analysis and object detection. With the experience and knowledge of our group, we could add meaningful application scenarios and end user tutorials.

    Team: Kristian Manthey, Dominik Rueß, Silvio Tristram, Christian Kaptur, Sourabh Bodas,Konstantinos Amplianitis, Kevin Buchwinkler, Malte Müller-Rowold

    Reference Link