Proseminar: 3D Image Processing

Instructor Robert Pech, Isabel Patiño
Office hour To arrange
Regular meeting time Wednesday, 10 s.t., A302
Effort 2 SWS, 4 LP
Starts May 6, 2015
Preliminary discussion April 17, 2015, A302
Room A302
Recurrence Irregular


Image processing is an important tool which is used in many applications as robotic self localization, 3D reconstruction and tracking, among others. In order to fullfil these applications, different methods of image processing have been developed, each approach is the result of the acquisition devices and sensors available in the time of development. In this seminar you will have the opportunity to study actual topics regarding to image processing and its applications. You will learn how to research the state of the art literature, summarize knowledge from multiple sources, present your work to your colleagues and report your findings in writing.

Every student will have 45 minutes to present his/her topic. For this you should prepare slides, for example with powerpoint, and a written report (15 to 20 pages). It is recommended to give the supervisor the slides at the latest one week before your presentation. After the presentation there will be a discussion with questions on the presentation. There will also be an evaluation (content, presentation style, design of slides, etc.) at the end. The written report has to be submitted within two weeks.

You will get some introductory literature for your topic. However it should be clear, that you have to look for more literature on your own to extend your topic. With an online-search from the university network (computer science pools, ZDV pools, VPN-client, etc.) you will have access to the most important journals. For your literature search it is recommended to use Google Scholar and Citeseer.


Datum Thema Betreuung Referent(in)
17.04.2015 Introduction Robert Pech, Isabel Patiño
08.05.2015 Monocular Visual SLAM [1] Robert Pech Lukas Holländer
15.05.2015 Structure from Motion [2] Robert Pech
22.05.2015 3D Reconstruction with RGBD Sensors [3] Isabel Patiño Adrian Friese
29.05.2015 Region Based Segmentation and Tracking Using a 3D model [4] Robert Pech
05.06.2015 Tracking People with Stereo Vision [5] Isabel Patiño Babette Jakobi
12.06.2015 Tracking People with RGBD Sensors [6] Isabel Patiño Arne Rantzen
19.06.2015 Body Gesture Recognition with RGBD Sensors [7] Isabel Patiño
26.06.2015 Hand Gesture Recognition with RGBD Sensors [8] Isabel Patiño
03.07.2015 Three Dimensional Line Filter For Medical Images [9] Robert Pech Jens Vial
10.07.2015 Shape-from-Motion in minimally Invasive Surgery [10] Robert Pech

Zur Reservierung eines Themas tragen Sie sich bitte mit den Schaltflächen ganz rechts in der Termin-Tabelle ein.
Diese Anmeldung ersetzt nicht die offizielle Anmeldung über das Campus-System! Sie dient zur Vorreservierung der einzelnen Termine bis zur Vorbesprechung, an der Sie persönlich anwesend sein müssen.

Recommended Literature

[1]Monocular Visual SLAM. H. Durrant-Whyte, and Tim Bailey: Simultaneous Localization and Mapping (Part I and II) ,Robotics and Automation Magazine, pp. 99-110 (2006)
[2] Structure from Motion. Sammeer Agarwal, Noah Snavely, Ian Simon et al: Building Rome in a Day, Communications of the ACM, Vol.54 No. 10, pp. 105-112 (2011)
[3] 3D Reconstruction with RGBD Sensors. Shahram Izadi, David Kim , Otmar Hilliges et al: KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera, UIST'11 Proceedings of the 24th annual ACM symposium on User interface software and technology , pp. 559-568 (2011)
[4] Region Based Segmentation and Tracking Using a 3D Model. Victor A. Prisacariu, and Ian D. Reid: PWP3D: Real-Time Segmentation and Tracking of 3D Objects, International Journal of Computer Vision, pp. 335-354 (2012)
[5] Tracking People with Stereo Vision. Emina Petrovic, Adian Leu, Danijela Ristic-Durrand, and Vlastimir Nikolic: Stereo Vision-Based Human Tracking for Robotic Follower, International Journal of Advanced Robotic Systems, (2013)
[6] Tracking People with RGBD Sensors . Matteo Munaro, Filippo Baso, and Emanuele Menegatti: Tracking Prople within Groups with RGB-D Data, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2101-2107 (2012). Matthias Luber, Luciano Spinello, and Kai O. Arras: People Tracking in RGB-D Data with On-line Boosted Target models, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3844-3849 (2011)
[7] Body Gesture Recognition with RGBD Sensors. Jakub Konecny, Michael Hagara: One-Shot-Learning Gesture Recognition using HoG-HOF Features, Journal of Machine learning Research 15, pp. 2513-2532 (2014)
[8] Hand Gesture Recognition with RGBD Sensors. Zhou Ren, Junsong Yuan, Zhengyou Zhang: Robust Hand Gesture Recognition based on Finger-Earth Moves Distance with a Commodity Depth Camera, MM'11 Proceedings of the 19th ACM international conference on Multimedia, pp 1093-1096 (2011). A. Kurakin, Z.Thang, Z.Liu: A Real Time System for Dynamic Hand Gesture Recognition with a Depth Sensor, 20th European Signal Processing Conference (EUSIPCO 2012), pp. 1975-1979 (2012)
[9] Three Dimensional Line Filter For Medical Images. Yoshinobu Sato, Shin Nakajima, NObuyuki Shiraga et al: Three-dimensional Multi-scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images, Medical Image Analysis Vol2, pp. 143-168 (1998)
[10] Shape-from-Motion in minimally Invasive Surgery. Peter Mountney, Danail Stoyanov, and Guang-Zhong Yang: Recovering Tissue Deformation and Laparoscope Motion odr Minimally Invasive Surgery, Institute of Biomedical Engineering, Imperial College London, United Kingdom


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