Alexey Abramov
| Email: | A.Abramov
@physik3.gwdg.de
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Publications
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BibTeX:
@INPROCEEDINGS{pabramovschoeler2013,
author = {P., Jeremie and Abramov, A and Schoeler, M and Wörgötter, Florentin},
title = {Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds},
booktitle = {Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on},
year = {2013}
}
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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}
}
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BibTeX:
@INCOLLECTION{paponabramovwoergoetter2012,
author = {Papon, J and Abramov, A and Wörgötter, F},
title = {Occlusion Handling in Video Segmentation via Predictive Feedback},
booktitle = {Computer Vision – ECCV 2012. Workshops and Demonstrations},
year = {2012}
}
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BibTeX:
@INPROCEEDINGS{abramovpaponpauwels2012,
author = {Abramov, Alexey and Papon, Jeremie and Pauwels, K and Wörgötter, F and Dellen, B},
title = {Depth-supported real-time video segmentation with the Kinect},
booktitle = {IEEE workshop on the Applications of Computer Vision (WACV)},
year = {2012}
}
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BibTeX:
@INPROCEEDINGS{abramovpaponpauwels2012a,
author = {Abramov, A and Papon, J and Pauwels, K and Wörgötter, F and Babette, D},
title = {Real-time Segmentation of Stereo Videos on a Resource-limited System with a Mobile GPU},
booktitle = {IEEE Transactions on circuits and systems for video technology},
year = {2012}
}
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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}
}
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BibTeX:
@INPROCEEDINGS{abramovkulviciuswoergoetter2011,
author = {Abramov, A and Kulvicius, T and Wörgötter, F and Dellen, B},
title = {Real-Time Image Segmentation on a GPU},
booktitle = {Facing the Multicore-Challenge},
year = {2011}
}
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| Abstract: Efficient segmentation of color images is important for many applications in computer vision. Non-parametric solutions are required in situations where little or no prior knowledge about the data is available. In this paper, we present a novel parallel image segmentation algorithm which segments images in real-time in a non-parametric way. The algo- rithm finds the equilibrium states of a Potts model in the superparamag- netic phase of the system. Our method maps perfectly onto the Graphics Processing Unit (GPU) architecture and has been implemented using the framework NVIDIA Compute Unified Device Architecture (CUDA). For images of 256 × 320 pixels we obtained a frame rate of 30 Hz that demonstrates the applicability of the algorithm to video-processing tasks in real-time1 | |||
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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}
}
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| 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 | |||
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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}
}
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| 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 | |||
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