1. An Ontology for Qualitative Description of Images Zoe Falomir, Ernesto Jiménez-Ruiz, Lledó Museros, M. Teresa Escrig Cognition for Robotics Research (C4R2) Temporal Knowledge Base Group (TKBG) University Jaume I, Castellón (SPAIN)

2. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20092 Motivation (I)  Our group is applying Freksa’s Double Cross Orientation model to robotic navigation indoors.  Our robots use a laser sensor to find the landmarks of a room which are its corners and the corners of the obstacles inside the room.  Problem: sometimes a robot tries to localize itself inside a room and the geometry of the detected landmarks and its relative situation wrt the other landmarks is not enough to solve ambiguous situations.  Solution: to describe visually the landmarks of the room in order to differentiate easily between them. C1 C2

3. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20093 Motivation (II)  Our approach describes qualitatively any image, by describing:  the visual features (shape and colour) and  the spatial features (orientation and topology) of the objects contained in an image.  An ontology provides our qualitative description:  A formal representation of the knowledge inside the robot  A standard language to exchange information between agents  New information inferred by the reasoners Qualitative Image Description Ontology

4. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20094 Index 1. Qualitative Description of Images 1.1. Approach 1.2. Models of Shape, Colour, Topology and Orientation 1.3. Structure of the Description 1.4. A Case of Study 2. Ontology 2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box) 3. Results 3.1. Approach 3.2. New Knowledge Inferred from the Case of Study 4. Conclusion and Future Work

5. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20095 1.1. Approach Qualitative Image Description Colour graph-based segmentation Qualitative Models of Shape, Colour, Topology and Orientation Image Processing Algorithms 1. Qualitative Description of Images:

6. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20096 Qualitative Shape of relevant point j: KEC: {line-line, line-curve, curve-line, curve-curve, curvature-point} A: {very-acute, acute, right, obtuse, very-obtuse} TC: {very-acute, acute, semicircular, plane, very-plane} L: {much-shorter (msh), half-lenght (hl), quite-shorter (qsh), similar-lenght (sl), quite-longer (ql), double-lenght (dl), much- longer (ml)} C: {convex, concave} Topology Model: -Disjoint (x,y): -Touching (x, y): -Completedly_inside (x, y): -Container (x, y): -Neighbours: Objects with the same container 1.2.Models of Shape, Colour, Topology and Orientation Relative Orientation Fixed Orientation Qualitative Colour Tags: {black, dark-grey, grey, light-grey, white, red, yellow, green, turquoise, blue, violet} 1. Qualitative Description of Images:

7. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20097 1.3. Structure of the Description Qualitative Image Description Visual Description (1.. nRegions) Spatial Description (1.. nRegions) Topology (Region) Fixed Orientation (Region) Relative Orientation (Region) Shape (Region) Colour (Region) Containers Neighbours Reference Systems 1. Qualitative Description of Images:

8. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20098 1.4. A Case of Study 1. Qualitative Description of Images:

9. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 20099 Index 1. Qualitative Description of Images 1.1. Approach 1.2. Models of Shape, Colour, Topology and Orientation 1.3. Structure of the Description 1.4. A Case of Study 2. Ontology 2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box) 3. Results 3.1. Approach 3.2. New Knowledge Inferred from the Case of Study 4. Conclusion and Future Work

10. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200910 2. Ontology  Provides our qualitative description with:  A formal and explicit meaning to the qualitative labels.  A standard language to share information between agents.  New information inferred by the reasoners  Tools:  Ontology language: OWL 3  Editor: Protégé 4  Reasoners: FacT++ and Pellet  Knowledge layers: 1.Reference Conceptualization 2.Contextualized Descriptions 3.Ontology Facts  Assertional Knowledge Box (A-Box) Terminological Knowlege Box (T-Box)

11. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200911 2.1. Terminological Knowlege Box (T-Box) 2. Ontology:  Reference Conceptualization represents knowledge which is supposed to be valid for any application.

12. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200912 2. Ontology: 2.1. Terminological Knowlege Box (T-Box)  Contextualized Knowledge represents a concrete domain which is application oriented.

13. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200913 2.2. Assertional Knowledge Box (A-Box) 2. Ontology:  Ontology facts represent the individuals extracted from the description of the image.

14. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200914 Index 1. Qualitative Description of Images 1.1. Approach 1.2. Models of Shape, Colour, Topology and Orientation 1.3. Structure of the Description 1.4. A Case of Study 2. Ontology 2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box) 3. Results 3.1. Approach 3.2. New Knowledge Inferred from the Case of Study 4. Conclusion and Future Work

15. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200915 3.1. Approach 3. Results

16. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200916 3.2. New Knowledge Inferred 3. Results  Inferences:  Object 0  UJI_Lab_Wall  Objects 4, 6  UJI_Lab_Door

17. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200917 Index 1. Qualitative Description of Images 1.1. Approach 1.2. Models of Shape, Colour, Topology and Orientation 1.3. Structure of the Description 1.4. A Case of Study 2. Ontology 2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box) 3. Results 3.1. Approach 3.2. New Knowledge Inferred from the Case of Study 4. Conclusion and Future Work

18. Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 200918 4. Conclusions and Future Work  Our approach describes qualitatively any image using qualitative models of shape, colour, topology and orientation.  The qualitative description obtained is represented by an ontology, which provides our system with:  A formal representation of the knowledge inside the robot  A standard language to exchange information between agents  New knowledge inferred by the reasoners.  As future work, we intend to:  Extend our approach to integrate the reasoner inside the robot system.  Extend our ontology to characterize and classify more landmarks of the robot environment.

19. 1 October 2009 is the first birthday of… The mission of C-Robots is to provide a 100% fully autonomous solution to existing machinery automation. Our product is an intelligent and artificial brain for service robotics. It is composed of specific hardware and an intelligent software which combines traditional robotic solutions with the more advanced cognitive solutions to address specific requirements. A spin-off business of We are looking for partners to apply to European projects

20. Thank you for your attention Suggestions to improve our work?