Die drei besten Artikel aus den »Selected Readings in Computer Graphics« des Jahres 2012 wurden mit dem Best Paper Award in den Kategorien »Impact on Business«, »Impact on Society« und »Impact on Science« ausgezeichnet. In die engere Wahl kamen weitere sechs Artikel.

Die Gewinner in den jeweiligen Kategorien sind:

Impact on Business

René Berndt, Volker Settgast, Eva Eggeling, Ulrich Krispel, Christoph Schinko, Sven Havemann, Dieter W. Fellner:

Ring's Anatomy - Parametric Design of Wedding Rings

Impact on Society

Tobias Große-Puppendahl, Eugen Berlin, Marko Borazio:

Enhancing Accelerometer-Based Activity Recognition with Capacitive Proximity Sensing

Impact on Science

Anton Andriyenko, Konrad Schindler, Stefan Roth:

Discrete-Continuous Optimization for Multi-Target Tracking

Liste der Publikationen

Gewinner und die Publikationen, die in die engere Wahl gekommen sind.


Ackermann, Jens; Langguth, Fabian; Fuhrmann, Simon; Goesele, Michael:
Photometric Stereo for Outdoor Webcams
We present a photometric stereo technique that operates on time-lapse sequences captured by static outdoor webcams over the course of several months. Outdoor webcams produce a large set of uncontrolled images subject to varying lighting and weather conditions. We first automatically select a suitable subset of the captured frames for further processing, reducing the dataset size by several orders of magnitude. A camera calibration step is applied to recover the camera response function, the absolute camera orientation, and to compute the light directions for each image. Finally, we describe a new photometric stereo technique for non-Lambertian scenes and unknown light source intensities to recover normal maps and spatially varying materials of the scene.
Conference on Computer Vision and Pattern Recognition (CVPR) <30, 2012, Providence, RI, USA>
In: IEEE Computer Society: IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2012. New York: IEEE, 2012, pp. 262-269

DOI: 10.1109/CVPR.2012.6247684

Andriyenko, Anton; Schindler, Konrad; Roth, Stefan:
Discrete-Continuous Optimization for Multi-Target Tracking
The problem of multi-target tracking is comprised of two distinct, but tightly coupled challenges: (i) the naturally discrete problem of data association, i.e. assigning image observations to the appropriate target; (ii) the naturally continuous problem of trajectory estimation, i.e. recovering the trajectories of all targets. To go beyond simple greedy solutions for data association, recent approaches often perform multi-target tracking using discrete optimization. This has the disadvantage that trajectories need to be pre-computed or represented discretely, thus limiting accuracy. In this paper we instead formulate multi-target tracking as a discrete continuous optimization problem that handles each aspect in its natural domain and allows leveraging powerful methods for multi-model fitting. Data association is performed using discrete optimization with label costs, yielding near optimality. Trajectory estimation is posed as a continuous fitting problem with a simple closed-form solution, which is used in turn to update the label costs. We demonstrate the accuracy and robustness of our approach with state-of-the art performance on several standard datasets.
Conference on Computer Vision and Pattern Recognition (CVPR) <30, 2012, Providence, RI, USA>
In: IEEE Computer Society: IEEE Conference on Computer Vision and Pattern Recognition : CVPR 2012. New York: IEEE, 2012, pp. 1926-1933

DOI: 10.1109/CVPR.2012.6247893

Bernard, Jürgen; Ruppert, Tobias; Scherer, Maximilian; Kohlhammer, Jörn; Schreck, Tobias:
Content-Based Layouts for Exploratory Metadata Search in Scientific Research Data
Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.
ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) <12, 2012, Washington, DC, USA>
In: Association for Computing Machinery (ACM): JCDL 2012. Proceedings : 12th ACM/IEEE-CS Joint Conference on Digital Libraries. New York: ACM, 2012, pp. 139-148

DOI: 10.1145/2232817.2232844

Berndt, Rene; Settgast, Volker; Eggeling, Eva; Schinko, Christoph; Krispel, Ulrich; Havemann, Sven; Fellner, Dieter W.:
Ring's Anatomy - Parametric Design of Wedding Rings
We present a use case that demonstrates the effectiveness of procedural shape modeling for mass customization of consumer products. We show a metadesign that is composed of a few well-defined procedural shape building blocks. It can generate a large variety of shapes and covers most of a design space defined by a collection of exemplars, in our case wedding rings. We describe the process of model abstraction for the shape space spanned by these shapes, arguing that the same is possible for other shape design spaces as well.
International Conference on Creative Content Technologies (CONTENT) <4, 2012, Nice, France>
In: Sehring, Hans-Werner et al.: CONTENT 2012 : The Fourth International Conference on Creative Content Technologies. ThinkMind, 2012, pp. 72-78


Große-Puppendahl, Tobias; Berlin, Eugen; Borazio, Marko:
Enhancing Accelerometer-Based Activity Recognition with Capacitive Proximity Sensing
Activity recognition with a wearable accelerometer is a common investigated research topic and enables the detection of basic activities like sitting, walking or standing. Recent work in this area adds different sensing modalities to the inertial data to collect more information of the user's environment to boost activity recognition for more challenging activities. This work presents a sensor prototype consisting of an accelerometer and a capacitive proximity sensor that senses the user's activities based on the combined sensor values. We show that our proposed approach of combining both modalities significantly improves the recognition rate for detecting activities of daily living.
International Joint Conference on Ambient Intelligence (AmI) <3, 2012, Pisa, Italy>
In: Paterno, Fabio (Ed.) et al.: Ambient Intelligence : Third International Joint Conference, AmI 2012. Berlin; Heidelberg; New York: Springer, 2012. (Lecture Notes in Computer Science (LNCS) 7683), pp. 17-32

DOI: 10.1007/978-3-642-34898-3_2

Gutzeit, Enrico; Voskamp, Jörg:
Automatic Segmentation of Wood Logs by Combining Detection and Segmentation
The segmentation of cut surfaces from a stack of wood logs is a challenging task and leads to many problems. Wood logs theoretically have a certain shape and color, which is the main reason to apply object detection methods. But in real world images there are many disturbing factors, such as defects, dirt or non-elliptical logs. In this paper we mainly address the problem of wood and wood log segmentation by combining object detection with a graph-cut segmentation. We introduce an iterative segmentation procedure, which detects the stack of wood, segments foreground and background, and separates the logs. Our novel approach works fully automatically and has no restrictions on the image acquisition other than well visible log cut surfaces. All three steps of our approach are novel and could be applied on similar problems. We implemented and evaluated different methods and show that of these approaches, our methods leads to the best results.
International Symposium on Visual Computing (ISVC) <8, 2012, Rethymnon, Crete, Greece>
In: Bebis, George (Ed.) et al.: Advances in Visual Computing. 8th International Symposium, ISVC 2012 : Proceedings, Part I. Berlin, Heidelberg, New York: Springer, 2012. (Lecture Notes in Computer Science (LNCS) 7431), pp.252-261

DOI: 10.1007/978-3-642-33179-4_25

Landesberger, Tatiana von; Bremm, Sebastian; Andrienko, Natalia; Andrienko, Gennady; Tekusová, Maria:
Visual Analytics Methods for Categoric Spatio-Temporal Data
We focus on visual analysis of space- and time-referenced categorical data, which describe possible states of spatial (geographical) objects or locations and their changes over time. The analysis of these data is difficult as there are only limited possibilities to analyze the three aspects (location, time and category) simultaneously. We present a new approach which interactively combines (a) visualization of categorical changes over time; (b) various spatial data displays; (c) computational techniques for task-oriented selection of time steps. They provide an expressive visualization with regard to either the overall evolution over time or unusual changes. We apply our approach on two use cases demonstrating its usefulness for a wide variety of tasks. We analyze data from movement tracking and meteorologic areas. Using our approach, expected events could be detected and new insights were gained.
IEEE Symposium on Visual Analytics Science and Technology (VAST) <7, 2012, Seattle, WA, USA>
In: Santucci, Giuseppe (Ed.) et al.: IEEE Conference on Visual Analytics Science and Technology 2012. Proceedings : VAST 2012. New York: IEEE Press, 2012, pp. 183 -192

DOI: 10.1109/VAST.2012.6400553

Steger, Sebastian; Sakas, Georgios:
FIST: Fast Interactive Segmentation of Tumors
Automatic segmentation methods for tumors are typically only suitable for a specific type of tumor in a specific imaging modality and sometimes lack in accuracy whereas manual tumor segmentation achieves the desired results but is very time consuming. Interactive segmentation however speeds up the process while still being able to maintain the accuracy of manual segmentation. This paper presents a novel method for fast interactive segmentation of tumors (called FIST) from medical images, which is suitable for all somewhat spherical tumors in any 3d medical imaging modality. The user clicks in the center of the tumor and a belief propagation based iterative adaption process is initiated, thereby considering image gradients as well as local smoothness priors of the surface. During that process, instant visual feedback is given, enabling to intervene in the adaption process by sketching parts of the contour in any cross section. The approach has successfully been applied to the segmentation of liver tumors in CT datasets. Satisfactory results could be achieved in 15.21 seconds on the average. Further trials on oropharynx tumors, liver tumors and the prostate from MR images as well as lymph nodes and the bladder from CT volumes demonstrate the generality of the presented approach.
International Workshop on Computational and Clinical Applications in Abdominal Imaging <3, 2011, Toronto, Canada>
In: Yoshida, Hiroyuki (Ed.) et al.: Abdominal Imaging: Computational and Clinical Applications : Third International Workshop Held in Conjunction with MICCAI 2011. Berlin, Heidelberg, New York: Springer, 2012. (Lecture Notes in Computer Science (LNCS) 7029), pp. 125-132

DOI: 10.1007/978-3-642-28557-8_16

Wesarg, Stefan; Kirschner, Matthias; Becker, Meike; Erdt, Marius; Kafchitsas, Konstantinos; Khan, M. Fawad:
Dual-energy CT-based Assessment of the Trabecular Bone in Vertebrae
Background: Osteoporosis can cause severe fractures of bone structures. One important indicator for pathology is a lowered bone mineral density (BMD) - conventionally assessed by dual-energy X-ray absorptiometry (DXA). Dual-energy CT (DECT) - being an alternative that is increasingly used in the clinics - allows the computation of the spatial BMD distribution. Objectives: Using DECT, the trabecular bone of vertebrae is examined. Several analysis methods for revealing the bone density distribution as well as appropriate visualization methods for detecting regions of lowered BMD are needed for computer-assisted diagnosis (CAD) of osteoporosis. The hypothesis that DECT is better suited than DXA for the computation of local BMD is investigated. Methods: Building on a model of the interaction of X-rays with bone tissue, novel methods for assessing the spatial structure of the trabecular bone are presented. CAD of DECT image data is facilitated by segmenting the regions of interest interactively and with an Active Shape Model, respectively. The barycentric space of fractional volumes is introduced as a novel means for analyzing bone constitution. For 29 cadaver specimens, DECT as well as DXA has been examined. BMD values derived from both modalities are compared to local force measurements. In addition, clinical data from two patients who underwent DECT scanning for a different reason is analyzed retrospectively. Results: A novel automated delineation method for vertebrae has been successfully applied to DECT data sets. It is shown that localized BMD measurements based on DECT show a stronger linear correlation (R² = 0.8242, linear regression) to local force measurements than density values derived from DXA (R² = 0.4815). Conclusions: DECT based BMD assessment is a method to extend the usage of increasingly acquired DECT image data. The developed DECT based analysis methods in conjunction with the visualization provide more detailed information for both, the radiologist and the orthopedist, compared to standard DXA based analysis.
In: Methods of Information in Medicine, Vol.51 (2012), 5, pp. 398-405

DOI: 10.3414/ME11-02-0034