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# Scene Identification & Tagging (SIT) | ||
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A semantic algorithm to learn and recognise composition ob objects based on their qualitative spatial relations. | ||
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## Dependences | ||
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This implementation depends on [OWLOOP](https://github.com/EmaroLab/owloop). | ||
It is implemented within the Robotic Operative System (ROS) Java bridge. | ||
The compilation is based on Gradle and you can find a complete list of dependencies versions | ||
in the [buld.gradle](https://github.com/EmaroLab/scene_identification_tagging/blob/sit_owloop/sit/build.gradle) file. | ||
Check out also the | ||
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## Documentation | ||
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The [resource](https://github.com/EmaroLab/scene_identification_tagging/tree/sit_owloop/resources/) | ||
folders contains the Javadoc documentation in the as well as the ontology that represents the tbox | ||
of this implementation. | ||
Check also the [Test](https://github.com/EmaroLab/scene_identification_tagging/blob/sit_owloop/sit/src/main/java/it/emarolab/scene_identification_tagging/Test.java) | ||
runnable class to see how the algorithm can be implemented by using this API. | ||
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## Algorithm | ||
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The algorithm is based on an OWL ontology, the Pellet reasoner and couple of mapping functions. | ||
Its main purposes is to describe primitive objects though geometric coefficients that | ||
define their shape. Than, qualitative spatial relations, such as: *right/left*, *front/behind*, *above/below*, | ||
*parallel*, *perpendicular* and *coaxial* are computed symbolically. Those are mapped in an concrete scene | ||
representation (i.e.: an individual). The recognition phase is based on instance checking by looking | ||
for the abstract scene representation (i.e.: classes) that classify the scene individual. | ||
If those does not exist the algorithm is able to use the concrete scene as a template to | ||
learn its abstract class to be used for further classification. Noteworthy, the | ||
system is automatically able to reason about similarity between learned scene. | ||
Also, it can be the case that a very complex scene is recognised by a relative small number of relation | ||
that hold (i.e.: a sub scene). To discriminate when those differences are too height, and | ||
trigger a new learning procedure, the concept of *confidence* is introduce as a number within [0,1]. | ||
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### Contacts | ||
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Work are progressing to give you more details about the algorithm. | ||
In the middle while, for any information, support, discussion or comments do not hesitate to contact me through this Github repository or at: | ||
[[email protected]](mailto:[email protected]), |
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