KEYNOTE SPEAKERS

Prof. Gunther Notni
Fraunhofer IOF, Jena, Germany
Speech
Title: Applications of Multimodal 3D-sensortechnologies
Abstract:
Multimodal sensors capture
and integrate diverse characteristics of a scene to
maximize information gain. Here multimodal imaging means
to combine 3D sensor data with image data from different
wavelength ranges such as color images - RGB, NIR, SWIR
(short-wave infrared), thermographic images,
polarization and multispectral image data.
Combining multimodal camera data with shape data from 3D
sensors is a challenging issue. Multimodal cameras,
e.g., hyperspectral cameras, or cameras outside the
visible light spectrum, e.g., thermal cameras or short
wave infrared have different resolutions, sensitivities
and image qualities.
The presentation will look at the various aspects that
need to be considered when setting up such systems.
These include in particular the calibration of
multi-camera / multi-sensor systems and how to
superimpose pixel-accurate multimodal image data onto 3D
data.
It will be discussed how multimodal 3D imaging is used
in various areas, such as medical technology for the
estimation of vital parameters, trust-based humanrobot
collaboration, material recognition for recycling and
forestry. Furthermore, it will be shown how this
technology and the use of thermal cameras can be used
for the 3D scanning of optically uncooperative surfaces
in the VIS and NIR spectral range, e.g., transparent or
specular materials without object preparation.
Biodata:
Gunther Notni studied
physics at the Friedrich Schiller University in Jena
from 1983-1988 and obtained his doctorate there in 1992.
He has worked at the Fraunhofer Institute for Applied
Optics and Precision Engineering IOF in Jena since 1992.
Here he headed the Optical Systems department from
1994-2020 and has been responsible for the Optical
Sensors & Metrology business unit on the Fraunhofer IOF
Board of Directors since 2020. In October 2014, he was
also appointed to a W3 professorship at TU Ilmenau,
where he heads the ‘Quality Assurance and Industrial
Image Processing’ department. His work focuses on the
development of optical 3D sensors and the principles of
multimodal and multispectral image processing and their
application in human-machine interaction, quality
assurance, medicine and forestry.

Prof. Jun Zhou
Griffith University, Australia
Speech
Title: Hyperspectral Computer Vision and Its Applications
Abstract:
Hyperspectral imagery
contains rich information on the spectral and spatial
distribution of object materials in a scene. Traditional
hyperspectral remote sensing methods mainly focus on
pixel-level spectral analysis. On the contrary, computer
vision has discovered colour, texture, and various
spatial and structural features of objects, but not
spectral information. It is necessary to bridge the gap
between spectral and spatial analysis to invent new
tools for effective image analysis and understanding.
This talk gives an overview of hyperspectral imaging
technology and material-based spectral-spatial analysis
techniques, and how they can be used to address
challenges in computer vision tasks. Several case
studies in object detection, image classification and
video tracking will be presented in this talk.
Biodata: Jun Zhou is a Professor and Deputy Head of School for the School of ICT at Griffith University, Australia. He received his Ph.D. degree from the University of Alberta, Canada, in 2006. Before joining Griffith University, he had taken research positions at the Australian National University and NICTA. His research interests include pattern recognition, hyperspectral imaging and computer vision with their applications to remote sensing, environment, agriculture and medicine. He was awarded the Australian Research Council Discovery Early Career Researcher Award in 2012. Prof. Zhou has published more than 300 papers in leading image processing, computer vision and remote sensing journals and conferences. He is an associate editor of five international journals, including IEEE TGRS and Pattern Recognition. He is the President of the Australian Pattern Recognition Society.

Dr. Auxi Padron
Instituto de Astrofísica de Canarias (IAC), USA
(On behalf of Prof. Jeff Kuhn, University of Hawaii,
USA)
Speech Title:
Revolutionizing the Next Generation of Ground-Based
Telescopes with Machine Learning
Abstract:
Telescopes are our windows
to the universe, and ensuring that they capture clear,
sharp images is essential for exploring distant worlds.
At the Laboratory for Innovation in Opto-Mechanics
(LIOM) of the Instituto de Astrofísica de Canarias, we
are developing the Small ExoLife Finder (SELF), a
3.5-meter prototype telescope. SELF serves as a testbed
for innovative technologies that will eventually be
implemented in the future ExoLife Finder (ELF) — a
planned 30-meter-class telescope designed to detect
signs of life on exoplanets.
A major innovation in SELF is the application of machine
learning (ML) to enable autonomous telescope alignment.
We generate synthetic datasets from a detailed optical
model of the telescope and use these to train deep
neural networks. These networks learn to precisely infer
the positions of the mirror actuators from telescope
images, and subsequently, effectively teaching the
system how to adjust the mirrors for optimal image
resolution. This method not only helps us determine the
ideal number and arrangement of actuators, but also
allows us to test whether a neural network trained on
simulated data can successfully align the telescope
under real-world conditions.
In addition, ML plays a key role in correcting
distortions caused by Earth’s turbulent atmosphere. One
strategy uses real-time images from the telescope to
provide feedback to fast-moving secondary mirrors for
coarse corrections. A complementary approach for fine
corrections employs integrated photonic devices — such
as photonic lanterns and multimode fibers — which act as
sensors to capture the complex effects of atmospheric
aberrations. Advanced ML algorithms are used to decode
the nonlinear behavior of these devices in real time,
enabling precise and rapid corrections.
We are also applying neural networks to reconstruct
surface maps of exoplanets. By learning to recognize
distinct reflective features from simulated data, our
models aim to identify biosignatures as well as features
like vegetation, deserts, and ice caps.
Together, these advances will enable us to build larger
and more powerful optical telescopes, opening new
pathways for discovering and studying distant planets in
our quest to find signs of life.
Biodata: Dr. Auxiliadora Padrón Brito holds a Bachelor's degree in Physics (2012) and a Master's in Astrophysics (2015) from the University of La Laguna, and a PhD in Photonics (2021) from the Institute of Photonic Sciences (ICFO), where she also worked as a postdoctoral researcher. Her research focused on quantum optics experiments with cold Rydberg atoms for quantum communication protocols. She is currently a postdoctoral researcher at the Instituto de Astrofísica de Canarias (IAC), where she works on integrated photonics for cophasing and wavefront sensing of the Small ExoLife Finder (SELF). Her work also involves collaborating with machine learning experts to interpret the behavior of photonic systems. Auxiliadora is passionate about making science more accessible and inclusive. She has organized outreach talks, developed interactive learning experiences, and helped coordinate a summer school on quantum communication. She is actively involved in content creation and communication strategy at the Laboratory for Innovation in Optomechanics (LIOM), and also holds a Master’s in Teacher Training.