Visual Computing Talks

    Tuesday 2. May 2017, 13:00

    Title: Simulating Minimally Invasive Cancer Treatment - A Retrospective Summary of 9 Years of Research
    Speaker: Philip Voglreiter, ICG
    Location: ICG Seminar Room
    Abstract: Methods for Minimally Invasive Canter Treatment present an important alternative for traditional surgical resection of tumors. However, predicting the outcome of such techniques is difficult, even for experienced interventional radiologists. Consecutively, accurate simulation can help to decide whether tumors are safely destroyed in these interventions. This is not only crucial for affected patients, but can also help cutting potential costs for necessary follow-up treatment.
    In this talk, I will provide an overview of the research conducted in three EU projects, involving a total of five technical institutions and five medical universities. While two of these projects (Imppact and GoSmart) successfully finished already, the third (ClinicImppact) is still running until June 2017. The timeline of the interdisciplinary work consists of many achievements. Amongst others, our consortium achieved early fundamental results in biomechanical properties and optimized medical routines for improved accuracy of simulating treatment. However, this talk will focus on the technical achievements. I will present a number of image processing, visualization and computational simulation techniques which culminate into technical environments for simulating minimally invasive cancer treatment. One of these environments focuses on computational performance. We achieve high accuracy simulation which is up to an order of magnitude faster than real-time treatment. This indicates potential usefulness as a decision-making tool for treatment planning in the clinical routine. The second application aims at comparing different treatment methods, devices and protocols. In a web-based environment, we provide a testbed for medical experts, researchers from biomechanical simulation and device vendors. Contributors can easily implement and evaluate novel techniques for simulation, protocols for treatment or algorithms implemented in devices and benchmark them with established techniques or results of real treatment. By sharing the techniques among users, interventional radiologists can simulate treatment with a multitude of devices and protocols, and pick the optimal method for an individual patient.

      Tuesday 25. April 2017, 13:00

      Title: Where Virtual Meets Real: Perceptually-Driven Inputs for New Output Devices
      Speaker: Piotr Didyk, Max Planck Institute for Informatics & Saarland University
      Location: ICG Seminar Room
      Abstract: There has been a tremendous increase in quality and number of new
      output devices, such as stereo and automultiscopic screens, portable
      and wearable displays, and 3D printers. Some of them have already
      entered the mass production and gained a lot of users’ attention;
      others will follow this trend promptly. Unfortunately, abilities of
      these emerging technologies outperform capabilities of methods and
      tools for creating content. Also, the current level of understanding
      of how these new technologies influence user experience is
      insufficient to fully exploit their advantages. In this talk, I will
      demonstrate that careful combinations of new hardware, computation,
      and models of human perception can lead to solutions that provide a
      significant increase in perceived quality. More precisely, I will show
      how careful rendering of frames can improve spatial resolution beyond
      physical capabilities of display devices. Next, I will discuss
      techniques for overcoming limitations of current 3D displays. In the
      context of 3D printing, I will present methods for specifying objects
      for multi-material 3D printing.

      Short Bio: Piotr Didyk is an Independent Research Group Leader at the Cluster of
      Excellence on ”Multimodal Computing and Interaction” at the Saarland
      University (Germany), where he is leading a group on Perception,
      Display, and Fabrication. He is also appointed as a Senior Researcher
      at the Max Planck Institute for Informatics. Prior to this, he spent
      two years as a postdoctoral associate at Massachusetts Institute of
      Technology (MIT). In 2012, he obtained his Ph.D. from the Max Planck
      Institute for Informatics and the Saarland University for his work on
      perceptual display. During his studies, he was also a visiting student
      at MIT. His research interests include human perception, new display
      technologies, image and video processing, and computational
      fabrication. He focuses on techniques that account for properties of
      the human sensory system and human interaction to improve perceived
      quality of the final images, videos, and 3D prints. More info:



        Tuesday 04. April 2017, 13:00

        Title: Sparse Label Propagation
        Speaker: Alexander Jung, Assistant Professor, Aalto University
        Location: ICG Seminar Room
        Abstract:In this talk, I present some of our most recent work on applying tools from compressed sensing to (semi-supervised) machine learning with massive network-structured datasets, i.e., big data over networks. We expect the use of compressed sensing ideas game changing as it was for digital signal processing. In particular, I will present a sparse label propagation algorithm which efficiently learns the labels for data points based on the availability of a few labeled training data points. This algorithm is inspired by compressed sensing methods and allows for a simple sufficient condition on the network structure and available label information such that accurate learning is possible.

          Tuesday 02. February 2016, 13:00

          Title: Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
          Speaker: Alexander Shekhovtsov, Christian Reinbacher
          Location: ICG Seminar Room
          Abstract: Dense image matching is a fundamental low-level problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithms in a consistent way. In the discrete setting, we propose a novel optimization algorithm that can be massively parallelized. In the continuous setting we tackle the problem of non-convex regularizers by a formulation based on differences of convex functions. The resulting hybrid discrete-continuous algorithm can be efficiently accelerated by modern GPUs and we demonstrate its real-time performance for the applications of dense stereo matching and optical flow.

          Tuesday 19. January 2016, 13:00

          Title: BaCoN: Building a Classifier from only N Samples
          Speaker: Georg Waltner
          Location: ICG Seminar Room
          Abstract: We propose a model able to learn new object classes with a very limited amount of training samples (i.e. 1 to 5), while requiring near zero run-time cost for learning new object classes. After extracting Convolutional Neural Network (CNN) features, we discriminatively learn embeddings to separate the classes in feature space. The proposed method is especially useful for applications such as dish or logo recognition, where users typically add object classes comprising a wide variety of representations. Another benefit of our method is the low demand for computing power and memory, making it applicable for object classification on embedded devices. We demonstrate on the Food-101 dataset that even one single training example is sufficient to recognize new object classes and considerably improve results over the probabilistic Nearest Class Means (NCM) formulation.