QDCA seminar

Qualitative Descriptions and Computational Applications (QDCA)  -- ME-711.20

SoSe 2019, Blockcourse, 2 SWS (ECTS: 4)  on 3-7 June 10:15h - 13:15h
WiSe 2018/2019, Blockcourse, 2 SWS (ECTS: 4)  on 18-22 February 10:15h - 13:15h

Cartesium Room 1.41 (1st floor)
Universität Bremen
Level: Master/Bachelor Degree (Computer Science or Digital Media)
Profil: KIKR and DMI
Language: English

Alternative study itinerary

If you would like to learn about these topics but you cannot attend the seminar, contact me to talk about 'independent studies'.


This seminar provides an introduction to Qualitative Descriptions and Reasoning from a Cognitive point of view. It is divided into 2 learning modules and 1 working module. The topic of each module is introduced as follows:

*Module I: If you were a robot, you would see the world pixelized through your camera. If you need to communicate with a human, the best you can use is language, so you need to define concepts/words. What concepts could you use for the human to understand you? Which qualitative models can you use to obtain concepts from your numerical sensor data?

*Module II: Psychological studies proved that people with good spatial cognition skills, are more successful in STEM (Science Technology Engineering and Math). Other studies say that we humans can train these spatial skills. Thus: How can we measure our spatial cognition skills? How can we improve them? Can we build videogames to improve them? which kind of feedback must players receive about their mistakes? Is it possible to use those logic algorithms in artificial agents?

*Module III: From all the contents, what is the most interesting topic for you? Which would you like to write about? What have you learned? What can you teach us?


Module I: Qualitative Descriptors applied to Images and Videos



2-Qualitative Shape Descriptor (QSD)

3-Qualitative Shape Similarity and Applications (SimQSD)

4- Qualitative Shape Composition (LogC-QSD)


5-Qualitative Colour Descriptor (QCD)

6-Fuzzy Colour Descriptor (Fuzzy-QCD)

7-Qualitative Color Similarity (SimQCD)


8-Qualitative Image Descriptor (QID)

9-Similarity of Qualitative Image Descriptors (SimQID)

10-Qualitative Spatial Descriptor for Modelling Group-Robot Interactions (QS-GRI)


11-Qualitative Descriptor of Movement (QMD)

12-Qualitative Descriptor of 3D Scenes (QSn3D)

Module II: Qualitative Descriptors applied in Videogames


13-Spatial Cognition and Perceptual Ability tests

14-Qualitative 3D Model based on Depth

15-Qualitative Model for Paper Folding

16-Qualitative Model for Object Rotation

Module III: Final work

 in two months

17-Students' Presentations of their Essays

Assessment and Final Evaluation

To receive credits for this course must: (i) to attend the talks, (ii) write an essay about the topic of the seminar; (iii) to present the essay in a talk. Attendance to the classes will account for 40%. Presentations should be well-prepared, well-informed, and serve to help your classmates understand the facts and issues connected with the topic of your essay. It should enable your classmates to ask interesting questions about it. Ideally, plan on a 20 min duration for your presentation and a subsequent discussion. Presentations will account for 30% of your overall grade. The final essay will count for 30% of your overall grade.

For more information, please contact:

Dr.-Ing. Zoe Falomir Llansola
Cartesium Building, Office 3.54 (3rd floor)
E-mail: zfalomir at uni-bremen dot de


References for Module I

Falomir Z., Museros L., Gonzalez-Abril L. (2015), A Model for Colour Naming and Comparing based on Conceptual Neighbourhood. An Application for Comparing Art Compositions, Knowledge-Based Systems, 81: 1-21. DOI: http://doi.org/10.1016/j.knosys.2014.12.013

Museros L., Falomir Z., Sanz I., Gonzalez-Abril L. (2015), Sketch Retrieval based on Qualitative Shape Similarity Matching: Towards a Tool for Teaching Geometry to Children, AI Communications, 28 (1): 73—86. DOI: http://doi.org/10.3233/AIC-140614

Falomir Z., Gonzalez-Abril L., Museros L., Ortega J. (2013), Measures of Similarity between Objects from a Qualitative Shape Description, Spatial Cognition and Computation, 13 (3): 181–218. DOI: http://doi.org/10.1080/13875868.2012.700463

Falomir Z., Museros L., Gonzalez-Abril L., Velasco F. (2013), Measures of Similarity between Qualitative Descriptions of Shape, Colour and Size Applied to Mosaic Assembling, J. Vis. Commun. Image R. 24 (3): 388–396. DOI: http://doi.org/10.1016/j.jvcir.2013.01.013

Falomir Z., Olteteanu A. (2015), Logics based and Qualitative Descriptors for Scene Understanding, Neurocomputing, 161: 3-16, SI: Recognition and Action for Scene Understanding, DOI: http://doi.org/10.1016/j.neucom.2015.01.074.

References for Module II

N. Newcombe, Picture this: Increasing math and science learning by improving spatial thinking, American Educator, vol. 34, no. 2, pp. 29–35, 2010.

S. A. Sorby, Educational research in developing 3D spatial skills for engineering students, International Journal of Science Education 31 (3) (2009) 459–480. doi:10.1080/09500690802595839.

Z. Falomir and E. Oliver (2016), Towards testing a Qualitative Descriptor of 3D Objects using a Computer Game Prototype, International Workshop on Models and Representations in Spatial Cognition (http://spatial.cs.illinois.edu/2016workshop/index.html), Delmenhorst, Germany, 3-4 March 2016.

Z. Falomir and E. Oliver (2016), Q3D-Game: A Tool for Training User’s 3D Spatial Skills, Symposium on Future Intelligent Educational Environments and Learning, SOFIEE (www.sofiee.org), London, UK, 12-13 September 2016, in press.

Z. Falomir (2016). Towards a qualitative descriptor for paper folding reasoning. Proceedings of the 29th International Workshop on Qualitative Reasoning, co-located at Int. Joint Conf. on Artificial Intelligence (IJCAI), New York, USA. https://ivi.fnwi.uva.nl/tcs/QRgroup/qr16/program.html