Abstract
In this paper we describe a novel approach to perform senescense reversal on photos of leaves based on Conditional Generative Adversarial Networks, which have been used succesfully to perform similar tasks on faces of humans and other picture to picture translations. We show that their use can lead to a valid solution to this problem, as long as the task of creating a large and comprehensive dataset is surpassed. Additionally, we present a new dataset that consists of 120 paired photos of leaves manually collected for this work, in their fresh and senescenced states. We used the structure similarity index to compare the ground truth with the generated images and yielded an average of 0.9.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, pp. 658–666 (2016)
Antipov, G., Baccouche, M., Dugelay, J.L.: Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2089–2093. IEEE (2017)
Candussi, A., Candussi, N., Höllerer, T.: Rendering realistic trees and forests in real time. In: Eurographics (Short Presentations), pp. 73–76. Citeseer (2005)
Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., Joly, A.: Going deeper in the automated identification of Herbarium specimens. BMC Evol. Biol. 17(1), 181 (2017)
Carranza-Rojas, J., Mata-Montero, E.: On the significance of leaf sides in automatic leaf-based plant species identification. In: 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), pp. 1–6, November 2016. https://doi.org/10.1109/CONCAPAN.2016.7942341
Chi, X., Sheng, B., Chen, Y., Wu, E.H.: Physically based simulation of weathering plant leaves. Chin. J. Comput. 32, 221–230 (2009)
Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 359(1444), 655–667 (2004)
Goëau, H., et al.: PlantNet participation at lifeclef2014 plant identification task. In: CLEF2014 Working Notes. Working Notes for CLEF 2014 Conference, Sheffield, UK, 15–18 September 2014, pp. 724–737. CEUR-WS (2013)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Guan, R., Wan, Y.: An improved unsharp masking sharpening algorithm for image enhancement. In: Eighth International Conference on Digital Image Processing (ICDIP 2016), vol. 10033, p. 100332A. International Society for Optics and Photonics (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Rolland, J.P., Vo, V., Bloss, B., Abbey, C.K.: Fast algorithms for histogram matching: application to texture synthesis. J. Electron. Imaging 9(1), 39–45 (2000)
Jeong, S., Park, S.H., Kim, C.H.: Simulation of morphology changes in drying leaves. In: Computer Graphics Forum, vol. 32, pp. 204–215. Wiley Online Library (2013)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017)
Mata-Montero, E., Carranza-Rojas, J.: Automated plant species identification: challenges and opportunities. In: Mata, F.J., Pont, A. (eds.) WITFOR 2016. IAICT, vol. 481, pp. 26–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44447-5_3
Miao, T., Zhao, C., Guo, X., Lu, S., Wen, W., et al.: Simulation of plant leaf color based on relative content of chlorophyll. Nongye Jixie Xuebao = Trans. Chin. Soc. Agric. Mach. 45(8), 282–287 (2014)
Silva, P., Yue, Y., Chen, B.Y., Nishita, T.: Simulating plant color aging taking into account the sap flow in the venation (2012)
Wang, Z., Bovik, A.C.: Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)
Wang, Z., Tang, X., Luo, W., Gao, S.: Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7939–7947 (2018)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Villacis-Llobet, J., Lucio-Troya, M., Calvo-Navarro, M., Calderon-Ramirez, S., Mata-Montero, E. (2020). A First Glance into Reversing Senescence on Herbarium Sample Images Through Conditional Generative Adversarial Networks. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-41005-6_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41004-9
Online ISBN: 978-3-030-41005-6
eBook Packages: Computer ScienceComputer Science (R0)