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A framework for the automatic annotation of car aesthetics

Published online by Cambridge University Press:  22 January 2007

CHIARA E. CATALANO ,
FRANCA GIANNINI ,
MARINA MONTI  and
GIULIANA UCELLI
CHIARA E. CATALANO
Affiliation:
Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Genova, Italy
FRANCA GIANNINI
Affiliation:
Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Genova, Italy
MARINA MONTI
Affiliation:
Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Genova, Italy
GIULIANA UCELLI
Affiliation:
Fondazione Graphitech, Trento, Italy
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Abstract

The design of a new car is guided by a set of directives indicating the target market, specific engineering, and aesthetic constraints, which may also include the preservation of the company brand identity or the restyling of products already on the market. When creating a new product, designers usually evaluate other existing products to find sources of inspiration or to possibly reuse successful solutions. In the perspective of an optimized styling workflow, great benefit could be derived from the possibility of easily retrieving the related documentation and existing digital models both from internal and external repositories. In fact, the rapid growth of resources on the Web and the widespread adoption of computer-assisted design tools have made available huge amounts of data, the utilization of which could be improved by using more selective retrieval methods. In particular, the retrieval of aesthetic elements may help designers to create digital models conforming to specific styling properties more efficiently. The aim of our research is the definition of a framework that supports (semi)automatic extraction of semantic data from three-dimensional models and other multimedia data to allow car designers to reuse knowledge and design solutions within the styling department. The first objective is then to capture and structure the explicit and implicit elements contributing to the definition of car aesthetics, which can be realistically tackled through computational models and methods. The second step is the definition of a system architecture that is able to transfer such semantic evaluation through the automatic annotation of car models.

Keywords

Car StylingGeometric ReasoningKnowledge ManagementShape Semantics
Type
Research Article
Information
AI EDAM , Volume 21 , Issue 1: 20th Anniversary Issue , January 2007 , pp. 73 - 90
Copyright
© 2007 Cambridge University Press

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References

REFERENCES

AIM@SHAPE. (n.d.). Advanced and innovative models and tools for the development of semantic based systems for handling, acquiring, and processing knowledge embedded in multidimensional digital objects. Key action: 2.3.1.7 Semantic-based knowledge systems, VI framework. European Network of Excellence, Accessed at http://www.aim-at-shape.net
Brunetti, G., Ucelli, G., Santos, P., De Amicis, R., & Stork, A., (2005). Handling shape semantics in virtual styling. Proc. Virtual Concept 2005, Biarritz, France, November 8–10.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698.Google Scholar
Catalano, C.E., Falcidieno, B., Giannini, F., & Monti, M. (2002). A survey of computer-aided modeling tools for aesthetic design. International Journal of Computing and Information Science in Engineering 2(1), 11–20.Google Scholar
Catalano, C.E. (2004). Feature-based methods for free-form surface manipulation in aesthetic engineering. PhD Thesis. Genoa, Italy: University of Genoa.
Cerri, A., Ferri, M., & Giorgi, D. (2005). A complete keypics experiment with size functions. Proc. CIVR 2005, Singapore. Lecture Notes in Computer Science (Leow, W.-K., Lew, M.S., Chua, T.-S., Ma, W.-Y., Chalsorn, L., & Bakker, E.M. Eds.), Vol. 3568, pp. 357–366. New York: Springer.
Chen, M., Cheng, Z., & Liu, Y. (2004). A robust algorithm of principal curve detection. ICPR 2004 Proc. 17th Int. Conf., Vol. 1, pp. 429–432, August 23–26.
Corney, J., Rea, H., Clark, D., Pritchard, J., Breaks, M., & Makleod, R. (2002). Coarse filters for shape matching. IEEE Computer Graphics and Applications 22(3), 65–74.Google Scholar
Dao, M.S., & De Amicis, R. (2005). CSSIR: car blueprints images retrieval using sketch and spatial information. Proc. TCNCAE 2005.
Dean, M., Connolly, D., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D.L., Patel-Schneider, P.F., & Stein, L.A. (2006). Owl web ontology language 1.0 reference. Presented at the World Wide Web Consortium, 2002. Accessed at http://www.w3.org/TR/owl-ref/
De Carlo, D., Finkelstein, A., Rusinkiewicz, S., & Santella, A. (2003). Suggestive contours for conveying shape. Proc. SIGGRAPH 2003, pp. 848–855.
De Luca, L., Véron, P., & Florenzano, M. (2005). Semantic-based modeling and representation of patrimony buildings. Proc. Workshop Towards Semantic Virtual Environments, pp. 27–36. Villars, Switzerland, March 16–18.
Farin, G., & Sapidis, N. (1989). Curvature and the fairness of curves and surfaces. IEEE Computer Graphics and Applications 9(2), 52–57.Google Scholar
Farin, G., Hoschek, J., & Kim, M.S. (Eds.). (2002). Handbook of Computer Aided Geometric Design. Amsterdam: Elsevier.
Fiorentino, M., De Amicis, R., Stork, A., & Monno, G. (2002). Spacedesign: conceptual styling and design review in augmented reality. Proc. ISMAR 2002 IEEE and ACM Int. Symp. Mixed and Augmented Reality, pp. 86–94, Darmstadt, Germany, September 30–October 1.
Giannini, F., Monti, M., & Podehl, G. (2004). Styling properties and features in computer aided industrial design. Proc. CAD 2004, Vol. 1(1–4), pp. 321–330.
Hagen, H., Hahmann, S., & Schreiber, T. (1995). Visualization and computation of curvature behaviour of freeform curves and surfaces. Computer-Aided Design, 27(7), 545–552.Google Scholar
Hernádvölgyi, I.T., Ucelli, G., Symonova, O., Delpero, L., & De Amicis, R. (2004). Shape semantics from shape context. Workshop Proc. Modelling and Retrieval of Context in KI 2004, pp. 85–96, Ulm, Germany, September 20–21.
Hsiao, S.W., & Wang, H.P. (1998). Applying the semantic transformation method to product form design. Design Studies 19, 309–330.
Java agent development framework (JADE). (2006). Accessed at http://jade.tilab.com/
Kazhdan, M., Funkhouser, T., & Rusinkiewicz, S. (2003). Rotation invariant spherical harmonic representation of 3D shape descriptors. Proc. Eurographics/ACM SIGGRAPH Symp. Geometry Processing, pp. 156–164, Aachen, Germany, June 23–25.
Kobbelt, L., Botsch, M., Kähler, K., Rössl, C., Schneider, R., & Vorsatz, J. (2000). Geometric modelling based on polygon meshes, Tutorial T4. Proc. Eurographics 2000.
Kopena, J., & Regli, W.C. (2003). Functional modeling of engineering designs for the semantic web. IEEE Data Engineering Bulletin 26(4), 55–61.Google Scholar
Körtgen, M., Park, G.J., Novotni, M., & Klein, R. (2003). 3D shape matching with 3D shape contexts. Proc. 7th Central European Seminar on Computer Graphics, April.
Krell, G., Tizhoosh, H.R., Lilienblum, T., & Moore, C.J. (1997). Fuzzy image enhancement and associative feature matching in radiotherapy. Proc. Int. Conf. Neural Networks (ICNN), pp. 1490–1495, Houston, TX, June.
Latecki, L.J., & Lakämper, R. (2000). Shape similarity measure based on correspondence of visual parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1185–1190.Google Scholar
Marr, D. (1982). Vision. San Francisco, CA: Freeman.
McCormack, J.P., Cagan, J., & Vogel, C.M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design Studies 25, 1–29.
Meribout, M., Ogura, T., & Nakanishi, M. (2000). On using the CAM concept for parametric curve extraction IEEE Transactions on Image Processing 9(12), 2126–2130.Google Scholar
Novotni, M., & Klein, R. (2003). 3D zernike descriptors for content based shape retrieval. Proc. Eighth ACM Symp. Solid Modeling and Applications, pp. 216–225, Seattle, WA, June 16–20.
Osada, R., Funkhouser, T., Chazelle, B., & Dobkin, D. (2001). Matching 3D models with shape distributions. Proc. Int. Conf. Shape Modeling and Applications, pp. 154–166, Genova, Italy, May 7–11.
Parent, P., & Zucker, S.W. (1989). Curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(8), 823–839.Google Scholar
Patrikalaliks, N.M., & Maekawa, T. (2000). Shape Interrogation for Computer Aided Design and Manufacturing. New York: Springer.
Podehl, G. (2002). Terms and measures for styling properties. Proc. Int. Design Conf., Dubrovnik.
Poitou, J.P. (2002). Emotion, Aesthetic, Geometry Relationship Analysis, FIORES II-WP2-T2.3, Summary Report GRD1-1999-10385.
Razdan, A., Liu, D., Bae, M., Zhu, M., Farin, G., Simon, A., & Henderson, M. (2001). Using geometric modeling for archiving and searching 3D archaeological vessels. Proc. CISST, pp. 451–457, Las Vegas, June 25–28.
Razdan, A., Rowe, J., Tocheri, M., & Sweitzer, W. (2002). Adding semantics to 3D digital libraries. In Lecture Notes in Computer Science, Vol. 2555, pp. 419–42. New York: Springer.
Rothwell, C.A., Zisserman, A.Z., Forsyth, D.A., & Mundy, J.L. (1992). Canonical Frames for planar object recognition. 2nd European Conf. Computer Vision, Lecture Notes in Computer Science, pp. 757–772. New York: Springer.
Salembier, P., Manjunath, B.S., & Sikora, R. (2002). Introduction to MPEG-7. New York: Wiley.
SRO. (2005). Street Racing Online Dictionary of Automotive Terms. Accessed at http://www.sromagazine.com/paris/dictionary
Szykman, S., Sriram, R.D., & Regli, W.C. (2001). The role of knowledge in next-generation product development systems. Journal of Computational and Information Science in Engineering 1(1), 3–11.Google Scholar
Ucelli, G., De Amicis, R., Conti, G., Brunetti, G., & Stork, A. (2005). Shape semantics and content management for industrial design and virtual styling. Proc. Workshop Towards Semantic Virtual Environments, pp. 127–137, Villars, Switzerland, March 16–18.
Wielinga, B.J., Schreiber, A.T., Wielemaker, J., & Sandberg, J.A.C. (2001). From thesaurus to ontology. Proc. 1st Int. Conf. Knowledge Capture (K-CAP'01), Victoria, British Columbia, Canada, October 22–23.
Yamada, Y. (1997). Clay modeling: techniques for giving three-dimensional form to idea. Car Styling Extra Issue 93.Google Scholar
Yanagisawa, H., & Fukuda, S. (2004). Global feature-based interactive reduct evolutional computation for aesthetic design. Proc. DETC'04, ASME Design Engineering Technical Conf. Computers and Information in Engineering Conf., Salt Lake City, UT, September 28–October 2.
Yoshizawa, S., Belyaev, A., & Seidel, H.P. (2005). Fast and robust detection of crest lines on meshes. ACM Symp. Solid and Physical Modeling, pp. 227–232, Cambridge, MA, June.