Un enfoque computacional para el análisis de composiciones artísticas
DOI:
https://doi.org/10.11606/gtp.v18i2.196288Palabras clave:
Arte computacional, análisis basado en píxeles, Codificación visual, MondrianResumen
Los nuevos enfoques que surgen del análisis de obras de arte con herramientas computacionales tienen el potencial de ofrecer diferentes perspectivas a las obras de arte recreadas en medios digitales. Este artículo pretende revelar las relaciones implícitas entre las composiciones de Mondrian con diferentes representaciones visuales. En el ámbito del estudio, las composiciones completadas entre 1938 y 1943, que tienen una fuerte relación geometría-color, se investigaron primero a través de un enfoque basado en píxeles. En el método de fragmentación empleado a seguir, las similitudes y diferencias se expresan con datos transferidos de píxeles a matrices numéricas en dos pasos diferentes: 1) Entre los artefactos en pares, y 2) Entre un artefacto y todos los demás artefactos seleccionados. La visualización de las matrices estuvo representada por mapas de color 2D y mapas de textura 3D. Estos estilos de interpretación permiten que las composiciones se expresen de lo general a lo específico y nuevamente de lo específico a lo general, adquiriendo un nuevo significado.
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ALVAREZ-RAMIREZ, J.; IBARRA-VALDEZ, C.; RODRIGUEZ, E. Fractal analysis of Jackson Pollock's painting evolution. Chaos, Solitons & Fractals, 83: 97-104, 2016
ANDRZEJEWSKI, D. et al. Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning. In: Computer Vision and Image Analysis of Art. International Society for Optics and Photonics, p. 75310G, 2010.
BOUNTIS, T.; FOKAS, A.S.; PSARAKIS, E.Z. Fractal analysis of tree paintings by Piet Mondrian (1872-1944). International Journal of Arts and Technology, 10.1: 27-42, 2017.
BOURACHED, A. et al. Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars. Electronic Imaging, 2021.14: 42-1-42-10, 2021.
CETINIC, E.; LIPIC, T.; GRGIC, S. A deep learning perspective on beauty, sentiment, and remembrance of art. IEEE Access, 7: 73694-73710, 2019.
CLEVELAND, Paul. Aesthetics and complexity in digital layout systems. Digital Creativity, 19.1: 33-50, 2018.
SILVA GARZA, Andrés Gómez de; LORES, Aram Zamora. Automating evolutionary art in the style of Mondrian. In: Genetic and Evolutionary Computation Conference. Proceedings. Springer, Berlin, Heidelberg. p. 394-395, 2004.
GARZA, ANDRÉS GÓMEZ DE SILVA; LORES, ARÁM ZAMORA. Evolutionary art revisited: Making the process fully automated. In Proceedings of the 5th World Scientific and Engineering Academy and Society (WSEAS) International Conference on Soft Computing, Optimization, Simulation and Manufacturing Systems (SOSM), 2005.
FEIJS, L. Divisions of the plane by computer: another way of looking at Mondrian's nonfigurative compositions. Leonardo, 37.3: 217-222, 2004.
FEIJS, L. A program for Victory Boogie Woogie. Journal of Mathematics and the Arts, 13.3: 261-285, 2019.
FEIJS, Loe. Analyzing the Structure of Mondrian's 1920-1940 Compositions. arXiv preprint arXiv:2011.00843, 2020.
FRANKE, H. W. Computer Graphics—Computer Art. Springer Science & Business Media, 2012.
GREENFIELD, Gary R. Art by computer program== programmer creativity. Digital Creativity, 2006, 17.01: 25-35.
HERTZMANN, A. Can computers create art?. In: Arts. Multidisciplinary Digital Publishing Institute. p. 18, 2018.
KIM, Diana, et al. Computational Analysis of Content in Fine Art Paintings. In: ICCC. p. 33-40, 2019.
KLEE, P.; MOHOLY-NAGY, S. Pedagogical sketchbook. London: Faber & Faber. 1953.
LEE, J.; LIU, Y.. Modelling Mondrians Design Processes and TheirArchitectural Associations Using Multilayer NeuralNetworks. 1998.
LEFEVRE, Kristen; DEWITT, David J.; RAMAKRISHNAN, Raghu. Mondrian multidimensional k-anonymity. In: 22nd International conference on data engineering (ICDE'06). IEEE, p. 25-25, 2006.
LOPES, A. M.; TENREIRO MACHADO, J. A. Complexity analysis of Escher’s art. Entropy, 21.6: 553, 2019.
MONDAL, J. Morphological Translation of Mondrian’s Neo-plastic 2D Compositions into 3D Embodiments using Shape Grammar. Proceedings of International Conference on Emerging Technologies In Architectural Design (ICETAD2019), Toronto, Canada, 2019.
NOLL, A. M. Human or machine: A subjective comparison of Piet Mondrian’s “Composition with Lines”(1917) and a computer-generated picture. The psychological record, 16.1: 1-10, 1966.
NOVAK, M. An Experiment in Computational Composition. New Ideas and Directions for the 1990's, 1989.
PARK, H. J. Stylistic Reproductions of Mondrian’s Composition With Red, Yellow, and Blue. In Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia. CAADRIA. 2020.
ROTZLER, W. Constructive Concepts: A History of Constructive Art from Cubism to the Present, Rizzoli, New York. 1989.
ROY, D. M. et al. The Mondrian Process. In: NIPS. p. 1377-1384. 2008
SHAMIR, L.; TARAKHOVSKY, J. A. Computer analysis of art. Journal on Computing and Cultural Heritage (JOCCH), 5.2: 1-11, 2012.
SIGAKI, Y.D.; PERC, M.; RIBEIRO, H. V. History of art paintings through the lens of entropy and complexity. Proceedings of the National Academy of Sciences, 115.37: E8585-E8594. 2018.
Skrodzki, M., & Polthier, K. (2018). Mondrian Revisited: A Peek into the Third Dimension. In Bridges 2018 Conference Proceedings (pp. 99-106). Tessellations Publishing.
SMIRNOV, S.; EGUIZABAL, A. Deep learning for object detection in fine-art paintings. In: 2018 Metrology for Archaeology and Cultural Heritage (MetroArchaeo). IEEE, p. 45-49. 2018.
THOMPSON, Michael. Computer Art: Pictures Composed of Binary Elements on a Square Grid. Leonardo, 271-276. 1977.
TUFTE, E. R.; GOELER, N. H.; BENSON, R. Envisioning information. Cheshire, CT: Graphics Press. 1990.
WANG, Y. et al. Metadata dependent Mondrian processes. In Proceedings: International Conference on Machine Learning. PMLR, p. 1339-1347. 2015.
WANG, Y.; XIE, R. Pixel-Based Approach for Generating Original and Imitating Evolutionary Art. Electronics, 9.8: 1311. 2020.
ZHANG, K.; HARRELL, S.; JI, X. Computational aesthetics: on the complexity of computer-generated paintings. Leonardo, 45.3: 243-248. 2012.
ZIV, Y. Parallels between Suprematism and the Abstract, Vector-Based Motion Graphics of Flash. In Proceedings: Intelligent Agent. 2006.
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Derechos de autor 2023 Feyza Nur Kocer Ozgun, Sema Alaçam
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