Name
Title | ||
|---|---|---|
Fast self-supervised on-line training for object recognition specifically for robotic applications |
Abstract | ||
|---|---|---|
Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific objects on-the-fly. We propose a fast and automatic system, which can extract and learn unknown objects with minimal human intervention by employing a two-level pipeline combining the advantages of RGB-D sensors for object extraction and high-resolution cameras for object recognition. Furthermore, we significantly improve recognition results with local features by implementing a novel keypoint orientation scheme, which leads to highly invariant but discriminative object signatures. Using only one image per object for training, our system is able to achieve a recognition rate of 79% for 18 objects, benchmarked on 42 scenes with random poses, scales and occlusion, while only taking 7 seconds for the training. Additionally, we evaluate our orientation scheme on the state-of-the-art 56-object SDU-dataset boosting accuracy for one training view per object by +37% to 78% and peaking at a performance of 98% for 11 training views. |
Year | Venue | Keywords |
|---|---|---|
2014 | 2014 International Conference on Computer Vision Theory and Applications (VISAPP) | Object Recognition,On-line Training,Local Feature Orientation,Invariant Features,Vision Pipeline |
Field | DocType | Volume |
Supervisor,Computer vision,3D single-object recognition,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Boosting (machine learning),Robot,Discriminative model,Benchmark (computing),Cognitive neuroscience of visual object recognition | Conference | 2 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Schoeler | 1 | 145 | 5.98 |
| Simon Christoph Stein | 2 | 29 | 1.29 |
| Jeremie Papon | 3 | 199 | 10.18 |
| Alexey Abramov | 4 | 217 | 8.99 |
| Florentin Wörgötter | 5 | 1304 | 119.30 |