MindSpaces: Art-driven Adaptive Outdoors and Indoors Design
DOI:
https://doi.org/10.55630/dipp.2019.9.43Keywords:
Virtual Reality (VR), Visual Behavior Analysis, 3D reconstruction, Emotion extraction, Style transfer, Text AnalysisAbstract
MindSpaces provides solutions for creating functionally and emotionally appealing architectural designs in urban spaces. Social media services, physiological sensing devices and video cameras provide data from sensing environments. State-of-the-Art technology including VR, 3D design tools, emotion extraction, visual behaviour analysis, and textual analysis will be incorporated in MindSpaces platform for analysing data and adapting the design of spaces.References
Avgerinakis, K., Meditskos, G., Derdaele, J., Mille, S., Shekhawat, Y., Fraguada, L., & ... & Wachtmeister, J. (2019). V4Design for enhancing architecture and video game creation. IEEE International Symposium on Mixed and Augmented Reality , (pp. 305- 309).
Bond, M. (2017). The hidden ways that architecture affects how you feel . pp. http://www.bbc.com/future/story/20170605-the-psychology-behind-your-citysdesign.
Chao, Y. W., Vijayanarasimhan, S., Seybold, B., Ross, D. A., Deng, J., & Sukthankar, R. (2018). Rethinking the faster r-cnn architecture for temporal action localization. IEEE Conference on Computer Vision and Pattern Recognition , (pp. 1130-1139).
Diba, A., Fayyaz, M., Sharma, V., Mahdi Arzani, M., Yousefzadeh, R., Gall, J., & Van Gool, L. (2018). Spatio-temporal channel correlation networks for action classification. European Conference on Computer Vision (ECCV) , (pp. 284-299).
Elad, M., & Milanfar, P. (2017). Style transfer via texture synthesis. IEEE Transactions on Image Processing , 2338-2351.
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. IEEE conference on computer vision and pattern recognition.
Gatys, L. A., Ecker, A. S., Bethge, M., Hertzmann, A., & Shechtman, E. (2017). Controlling perceptual factors in neural style transfer. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
González, G. F. (2016). Graph analysis of EEG resting state functional networks in dyslexic readers. Clinical Neurophysiology , 3165-3175.
Hara, K., Kataoka, H., & Satoh, Y. (2018). Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet. IEEE conference on Computer Vision and Pattern Recognition , (pp. 6546-6555).
Huang, X., & Belongie, S. (2017). Arbitrary style transfer in real-time with adaptive instance normalization. IEEE International Conference on Computer Vision.
Kyprianidis, J. E., Collomosse, J., Wang, T., & Isenberg, T. (2013). State of the" Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video. IEEE transactions on visualization and computer graphics, , 866-885.
Mille, S., Belz, A., Bohnet, B., & Wanner, L. (2018). Underspecified Universal Dependency Structures as Inputs for Multilingual Surface Realisation. 11th International Conference on Natural Language Generation , (pp. 199-209).
Sandryhaila, A., & Moura, J. M. (2014). Big data analysis with signal processing on graphs. IEEE Signal Processing Magazine , 80-90.
Shvets, A., Mille, S., & Wanner, L. (2018). Sentence Packaging in Text Generation from Semantic Graphs as a Community Detection Problem. . 11th International Conference on Natural Language Generation , (pp. 350-359).
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., & Paluri, M. (2018). A closer look at spatiotemporal convolutions for action recognition. IEEE conference on Computer Vision and Pattern Recognition , (pp. 6450-6459).
Wanner, L. A., Blat, J., Dasiopoulou, S., F. M., Fraga, T., & ... & Meditskos, G. (2017). Kristina: A knowledge-based virtual conversation agent. International conference on practical applications of agents and multi-agent systems , (pp. 284-295).