Dr. Eren Erdal Aksoy
| Group(s): | Computer Vision |
|---|---|
| Email: | eaksoye
@physik3.gwdg.de
|
| Phone: | +49 551/ 39 10764 |
| Website: | Aksoy |
Publications
Please click on the respective heading to sort the list accordingly
(e.g. click on 'year' to sort the list in descending order of year of publication).
| Author / Editor / Organization | Title |
Year |
Journal / Proceedings / Book |
|---|---|---|---|
|
|
|||
BibTeX:
@INPROCEEDINGS{paponabramovaksoy2012,
author = {Papon, J and Abramov, A and Aksoy, E and Wörgötter, Florentin},
title = {A modular system architecture for online parallel vision pipelines},
booktitle = {Applications of Computer Vision (WACV), 2012 IEEE Workshop on},
year = {2012}
}
|
|||
| Abstract: | |||
| Review: | |||
|
|
|||
BibTeX:
@ARTICLE{woergoetteraksoykrueger2012,
author = {Wörgötter, F and Aksoy, Eren Erdal and Krüger, N and Piater, J and Ude, A and Tamosiunaite, M},
title = {A Simple Ontology of Manipulation Actions based on Hand-Object Relations},
journal = {IEEE Transactions on Autonomous Mental Development. (In press)},
year = {2012}
}
|
|||
| Abstract: | |||
| Review: | |||
|
|
|||
BibTeX:
@INPROCEEDINGS{aksoydellentamosiunaite2011,
author = {Aksoy, Eren Erdal and Dellen, B and Tamosiunaite, M and Wörgötter, F},
title = {Execution of a Dual-Object (Pushing) Action with Semantic Event Chains},
booktitle = {IEEE-RAS Int. Conf. on Humanoid Robots},
year = {2011}
}
|
|||
| Abstract: | |||
| Review: | |||
|
|
|||
BibTeX:
@ARTICLE{aksoyabramovdoerr2011,
author = {Aksoy, Eren Erdal and Abramov, A and Dörr, J and Kejun, N and Dellen, B and Wörgötter, F},
title = {Learning the semantics of object-action relations by observation},
journal = {The International Journal of Robotics Research September},
year = {2011}
}
|
|||
| Abstract: | |||
| Review: | |||
|
|
|||
BibTeX:
@INPROCEEDINGS{abramovaksoydoerr2010,
author = {Abramov, A and Aksoy, E E and Dörr, J and Wörgötter, F and Pauwels, K and Dellen, B},
title = {3d semantic representation of actions from efficient stereo-image-sequence segmentation on GPUs},
booktitle = {5th International Symposium 3D Data Processing, Visualization and Transmission},
year = {2010}
}
|
|||
| Abstract: A novel real-time framework for model-free stereo-video segmentation and stereo-segment tracking is presented, combining real-time optical flow and stereo with image segmentation running separately on two GPUs. The stereosegment tracking algorithm achieves a frame rate of 23 Hz for regular videos with a frame size of 256 x 320 pixels and nearly real time for stereo videos. The computed stereo segments are used to construct 3D segment graphs, from which main graphs, representing a relevant change in the scene, are extracted, which allow us to represent a movie of e.g. 396 original frames by only 12 graphs, each containing only a small number of nodes, providing a condensed description of the scene while preserving data-intrinsic semantics. Using this method, human activities, e.g. and handling of objects, can be encoded in an efficient way. The method has potential applications for manipulation action recognition and learning, and provides a vision-front end for applications in cognitive robotics | |||
| Review: | |||
|
|
|||
BibTeX:
@INPROCEEDINGS{aksoyabramovwoergoetter2010,
author = {Aksoy, E E and Abramov, A and Wörgötter, F and Dellen, B},
title = {Categorizing object-action relations from semantic scene graphs},
booktitle = {2010 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2010}
}
|
|||
| Abstract: In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table of the action scene, which allows extracting object- action relations. The method is applied to several artificial and real action scenes containing limited context. The central novelty of this approach is that it is model free and needs a priori representation neither for objects nor actions. Essentially actions are recognized without requiring prior object knowledge and objects are categorized solely based on their exhibited role within an action sequence. Thus, this approach is grounded in the affordance principle, which has recently attracted much attention in robotics and provides a way forward for trial and error learning of object-action relations through repeated experimentation. It may therefore be useful for recognition and categorization tasks for example in imitation learning in developmental and cognitive robotics | |||
| Review: | |||
|
|
|||
BibTeX:
@ARTICLE{dellenaksoywoergoetter2009,
author = {Dellen, B and Aksoy, E E and Wörgötter, F},
title = {Segment Tracking via a Spatiotemporal Linking Process including Feedback Stabilization in an n-D Lattice Model},
journal = {Sensors},
year = {2009}
}
|
|||
| Abstract: Model-free tracking is important for solving tasks such as moving-object tracking and action recognition in cases where no prior object knowledge is available. For this purpose, we extend the concept of spatially synchronous dynamics in spin-lattice models to the spatiotemporal domain to track segments within an image sequence. The method is related to synchronization processes in neural networks and based on superparamagnetic clustering of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. The algorithm obeys detailed balance. This is an important property as it allows for consistent spin-transfer across subsequent frames, which can be used for segment tracking. Therefore, in the tracking process the correct equilibrium will always be found, which is an important advance as compared with other more heuristic tracking procedures. In the case of long image sequences, i.e. and movies, the algorithm is augmented with a feedback mechanism, further stabilizing segment tracking | |||
| Review: | |||