Qualitative Distances and Qualitative Description of Images for Indoor Scene Description and Recognition in Robotics
|Other Titles:||Qualitative Abstände und qualitative Beschreibung von Bildern zur Beschreibung und Erkennung von Innenraumszenen durch Robotern||Authors:||Falomir Llansola, Zoe||Supervisor:||Escrig, Maria Teresa||1. Expert:||Freksa, Christian||Abstract:||
The automatic extraction of knowledge from the world by a robotic system as human beings interpret their environment through their senses is still an unsolved task in Artificial Intelligence. A robotic agent is in contact with the world through its sensors and other electronic components which obtain and process mainly numerical information. Sonar, infrared and laser sensors obtain distance information. Webcams obtain digital images that are represented internally as matrices of red, blue and green (RGB) colour coordinate values. All this numerical values obtained from the environment need a later interpretation in order to provide the knowledge required by the robotic agent in order to carry out a task. Similarly, light wavelengths with specific amplitude are captured by cone cells of human eyes obtaining also stimulus without meaning. However, the information that human beings can describe and remember from what they see is expressed using words, that is qualitatively. The research work done in this thesis tries to narrow the gap between the acquisition of low level information by robot sensors and the need of obtaining high level or qualitative information for enhancing human-machine communication and for applying logical reasoning processes based on concepts. Moreover, qualitative concepts can be added a meaning by relating them to others. They can be used for reasoning applying qualitative models that have been developed in the last twenty years for describing and interpreting metrical and mathematical concepts such as orientation, distance, velocity, acceleration, and so on. And they can be also understood by human-users both written and read aloud. The first contribution presented is the definition of a method for obtaining fuzzy distance patterns (which include qualitative distances such as near , far , very far and so on) from the data obtained by any kind of distance sensors incorporated in a mobile robot and the definition of a factor to measure the dissimilarity between those fuzzy patterns. Both have been applied to the integration of the distances obtained by the sonar and laser distance sensors incorporated in a Pioneer 2 dx mobile robot and, as a result, special obstacles have been detected as glass window , mirror , and so on. Moreover, the fuzzy distance patterns provided have been also defuzzified in order to obtain a smooth robot speed and used to classify orientation reference systems into open (it defines an open space to be explored) or closed . The second contribution presented is the definition of a model for qualitative image description (QID) based on qualitative models of shape, colour, topology and orientation. This model can qualitatively describe any kind of digital image and is independent of the image segmentation method used. The QID model have been tested in two scenarios in robotics: (i) the description of digital images captured by the camera of a Pioneer 2 dx mobile robot and (ii) the description of digital images of tile mosaics taken by an industrial camera located on a platform used by a robot arm to assemble tile mosaics. In order to provide a formal and explicit meaning to the qualitative description of the images generated, a Description Logic (DL) based ontology has been designed and presented as the third contribution. Our approach can automatically process any random image and obtain a set of DL-axioms that describe it visually and spatially. And objects included in the images are classified according to the ontology schema using a DL reasoner. Tests have been carried out using digital images captured by a webcam incorporated in a Pioneer 2 dx mobile robot. The images taken correspond to the corridors of a building at University Jaume I and objects with them have been classified into walls , floor , office doors and fire extinguishers under different illumination conditions and from different observer viewpoints. The final contribution is the definition of a similarity measure between qualitative descriptions of shape, colour, topology and orientation. And the integration of those measures into the definition of a general similarity measure between two qualitative descriptions of images. These similarity measures have been applied to: (i) extract objects with similar shapes from the MPEG7 CE Shape-1 library; (ii) assemble tile mosaics by qualitative shape and colour similarity matching; (iii) compare images of tile compositions; and (iv) compare images of natural landmarks in a mobile robot world for their recognition.
|Keywords:||Sensor data integration; Fuzzy set theory; Qualitative representation and modelling; Image segmentation; Qualitative shape; Qualitative colour; Qualitative orientation; Qualitative topology; Ontology; Description Logics; Similarity; Conceptual Neighbourhood Diagrams; Interval Distances; Robotics.||Issue Date:||28-Nov-2011||URN:||urn:nbn:de:gbv:46-00103603-11||Institution:||Universität Bremen||Faculty:||FB3 Mathematik/Informatik|
|Appears in Collections:||Dissertationen|
checked on Oct 1, 2020
checked on Oct 1, 2020
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