Master’s theses

IMLEX students make a Master’s thesis, 30 ECTS, during the semester 4. Thesis work can be conducted in any of the consortium partner institutions or companies, in Europe or Japan. Topics are proposed by the consortium members, but students can agree on some tailored topic individually with academic supervisors. Each thesis is supervised and evaluated jointly by academic members of at least two different partner universities. Thesis presentations are held in August after the second study year.

Master’s theses, student cohort 2020

Yamato Koke: Zero-shot style transfer of Ukiyo-e images for landscape photographs
Valeria Denise Acevedo Donato: Low vision simulation in extended realities
Michael Makoto Martinsen: Breakthrough time depends on letter type and upright orientation: a pilot study using continuous flash suppression
Miguel da Lomba Magalḥes: Hyperspectral Image Fusion РA Comprehensive Review
Alexander Gaura: Imaging Pipeline of Spectral Filter Arrays for Color Image Visualization
Iacopo Catalano: A new 3D Surfel Mapping Framework based on Optimal Transport
Emmanuel Bustos Torres: Human Experience of AR/MR generated visual content
Kento Yamagata: Automatic Annotation for Non-road Scenes using RGB-D Information
Wengling Chen: Retinal imaging analysis with convolutional neural networks
Kenta Miyamoto: The congruency of color-sound crossmodal correspondence enhances/interferes with color and sound discrimination depending on the color category
Niladri Ganguly: Generation and characterization of elliptical ultrafast non-diffractive laser beams for application in laser processing
Alp Eren Aydin: Hybrid integration of III/V-based components on silicon on insulator platform by means of laser-assisted bonding for virtual and augmented reality applications
Ryo Isomura: Color gradation of natural objects: color trajectories from spectral sharpening due to concentration of pigments
Naoki Takahashi: Two Stage Manga Inpainting with Downsampling Fourier Convolutions
Khampasith Chanvongnaraz: Multi-Task Three Players GAN for Monocular Depth Estimation