An Ontology for Qualitative Description of Images Zoe Falomir, Ernesto Jiménez-Ruiz, Lledó...

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)

Zoe Falomir Llansola Spatial and Temporal Reasoning for Ambient Intelligence Systems at COSIT 2009

2

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.

C1C2


3

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

Index

1. Qualitative Description of Images1.1. Approach1.2. Models of Shape, Colour, Topology and Orientation1.3. Structure of the Description1.4. A Case of Study

2. Ontology2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box)

3. Results3.1. Approach3.2. New Knowledge Inferred from the Case of Study

4. Conclusion and Future Work


5

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

Qualitative Shape of relevant point j:<KEC(j), A(j) or TC(j), L(j), C(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}



7

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



8

1.4. A Case of Study



9

Index


2. Ontology2.1. Terminological Knowlege Box (T-Box)2.2. Assertional Knowledge Box (A-Box)




10

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: OWL3

Editor: Protégé 4

Reasoners: FacT++ and Pellet

Knowledge layers:1. Reference Conceptualization2. Contextualized Descriptions3. Ontology Facts Assertional Knowledge Box (A-Box)

Terminological Knowlege Box (T-Box)


11

2.1. Terminological Knowlege Box (T-Box)

2. Ontology:

Reference Conceptualization represents knowledge which is supposed to be valid for any application.


12

2. Ontology:2.1. Terminological Knowlege Box (T-Box)

Contextualized Knowledge represents a concrete domain which is application oriented.


13

2.2. Assertional Knowledge Box (A-Box) 2. Ontology:

Ontology facts represent the individuals extracted from the description of the image.


14

Index


2. Ontology2.1. Terminological Knowlege Box (T-Box)2.2. Assertional Knowledge Box (A-Box)




15

3.1. Approach

3. Results


16

3.2. New Knowledge Inferred

3. Results

Inferences:

Object 0 UJI_Lab_Wall

Objects 4, 6 UJI_Lab_Door


17

Index


2. Ontology2.1. Terminological Knowlege Box (T-Box) 2.2. Assertional Knowledge Box (A-Box)




18

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.

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

Thank you for your attention

Suggestions to improve our work?

An Ontology for Qualitative Description of Images Zoe Falomir, Ernesto Jiménez-Ruiz, Lledó...

Documents

Transcript of An Ontology for Qualitative Description of Images Zoe Falomir, Ernesto Jiménez-Ruiz, Lledó...