The research of our group focuses on the investigation of mechanisms and the underlying
structure of visual information processing in biological and technical systems (biological
and machine vision) as well as their adaptation to changing environments (visual
learning). The topics of our investigation primarily focus on mechanisms of visual
information processing (see details in section Research interests and facilities
below). The neural computational mechanisms that we use for the modeling of generating
complex visual behavior and adaptation also transfer to related processes such as auditory
processing and the control of (invertebrate) motor pattern generation.
Our research program utilizes an integrative approach based on the analysis of empirical
data derived from psychophysics, neuroscience and imaging studies, and the mathematical
and computational investigation of the underlying neural processes. We conduct psychophysical
experiments in our computational perception lab where we utilize 2D and 3D Computer
Graphics methods for synthetic image generation to create test stimuli and sequences.
Such data aims to simulate complex visual displays and animations, e.g., for spatio-temporal
grouping, surface perception, or spatial navigation in various environments. We also conduct
experiments using head and eye tracking facilities. Eye tracking particularly enables us to
record eye movement traces and length of fixations deployed to scenic objects. The results
of such experimental investigations (i) further guide our modeling to gain insights into the
computational mechanisms of brain function and (ii) advances application oriented
investigations into, e.g., attention processes for feature selection.
The results of neural modeling gain new insights into the computational mechanisms
underlying complex brain function and steer the development of new approaches and
mechanisms for computational vision, image processing and (space-variant) active vision.
These developments contribute new methods to several application areas
such as, e.g., vision in perceptual and attentive man-computer interfaces, biometric
systems, automotive technologies, medical image analysis and recognition, and visually-guided
robotics. The development of future emergent technology in human-computer interaction
(HCI) aims at developing advanced mechanisms endowing computers with more human-like
capabilities and performance. In order to provide a cross-disciplinary forum of joint
research activities we have founded an interdisciplinary
Competence Center
Perception and Interactive Technology
(PIT) (together with the Institutes
of Media Computing and Information Technology, respectively, at Ulm University). Here,
researchers and technology are brought together from various scientific disciplines such as,
e.g., computer science and engineering sciences, medical, psychological and neurosciences.
A brief summary of our overall research statement and recent research topics of our group
can be found on the
Vision Science @ Ulm University
poster.
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Modeling mechanisms of static surface perception
Surfaces of environmental objects create structure in the ambient light patterns that
enable visually guided interaction with the environment, such as, e.g., avoiding
obstacles, grasping objects, or locating and identifying members in a social group.
In order to develop a coherent model of core mechanisms and their interaction we
investigate, a,ong others, the neural mechanisms that underlie the detection of features,
the grouping of extended boundaries, or texture segregation, and the regularization of
such low- and mid-level vision processes. We particularly pursue an approach that
highlights the importance of feedforward and feed-back connections in neural network
architecture. |
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Modeling mechanisms of motion perception
The neural mechanisms underlying motion segregation and integration still remain unclear
to a large extent. Local motion estimates often remain ambiguous (aperture problem) when
lacking localized form components, such as corners or junctions. Even in the presence of
such features, local motion estimates may be erroneous if these were generated by mutual
occlusions of different objects. We have developed neural model mechanisms of visual
motion analysis to robustly disambiguate locally conflicting cues. Building upon these
local motion estimates large-field optical flow patterns are extracted to generate
distributed representations of neural activation. These responses contribute to the
perception of transparent motion and can be used to control visual tasks such as navigation
in complex 3D environments. |
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Neurodynamical modeling and perceptual psychophysics
We study the computational processes underlying perceptual and cognitive mechanisms
at different levels of abstraction, namely the dynamic behavior of ensembles of neurons
with their spatio-temporal firing patterns and as well as the dynamics at the system
level represented by the mean activities of model neurons (firing rates). The
mathematical modeling and computer simulations of such models help to understand the
mechanisms and functions underlying visually guided behavior in biological systems.
These results are further utilized to design new bioinspired and neuromorphic technology
to enhance, e.g., robotic and human-computer interface technology. We also conduct
psychophysical experiments in our perception laboratory to investigate behavior in human
perception and cognition. The data is again used to steer the modeling process. |
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Mechanisms of decision-making and perceptual learning
We investigate mechanisms underlying the perceptual learning of mechanisms for
complex feature extraction as well as velocity extraction. In addition, neural
decision-making processes in motion perception are investigated which serve as a
model to link neural data with perceptual behavior, such as, e.g., in pattern
discimination tasks. Furthermore, we will assess the influence of response modulation
through learning by utilizing large-scale neural network modelling. The approaches
considered here utilize biologically realistic learning mechanisms, namely, Hebbian
learning implemented by spike-timing dependent plasticity (STDP). The results of
these computational investigations in turn will have clinical impact on the development
of methods for the improvement of perceptual capabilities in rehabilitation. |
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Modeling attention, search, eye movements, and social communication
We investigate the role and neural mechanisms of spatial attention in the visual
perception of static and moving patterns. The visual detection of human heads and the
associated estimation of head pose and view direction provide core mechanisms to enable
the assessment of visual communication processes between members of a group. Within the
framework of neural architecture of feedforward and feed-back processing, we model the
generation of response patterns that are consistent with the biased competition
hypothesis of attention. We also employ eye tracking studies to monitor the active
motor processes involved in the overt gaze shifting to deploy focussed attention to
different locations in a scene. In addition, we pusue an approach to link form and
motion information at an intermediate level and to assign focussed attention to
spatio-temporal patterns that signal vision-based social communication. On a long-term
scale of investigation the analysis of body pose, head orientation and eye gaze helps
developing advanced mechanisms in human-computer interaction. |
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Space-variant vision
An important characteristic of the primate visual system is the space-variant mapping
of the visual field onto the cortex (with a small central region of highest resolution
(fovea) and decreasing resolution towards the periphery). This, in turn, necessitates
eye movements allowing rapid deployment of the high-resolution fovea to interesting
regions of the environment. Space-variant active vision is investigated for fixation,
smooth pursuit as well as investigating the advantages of reduced sampling at the same
time keeping a wide field of view. |
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Applications - Computer vision, image processing, and pattern recognition
We transfer biologically inspired mechanisms and models to algorithms that can be applied
in computer vision scenarios, as well as image processing, and pattern recognition and
classification approaches in various domains. The main application domains are in, e.g.,
the human-computer interaction, medical, and automotive systems research areas. |
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The computer as dialogue companion – Perception and interaction in
multi-user scenarios
(Funding: State of Baden-Wuerttemberg)
The project aims at developing novel mechanisms and principles of systems
architectures for intelligent human-computer interaction and evaluate these
by using psycho-physical and psycho-biological test methods. We aim at the
creation of advanced user interfaces with extended perceptual and interactive
capabilities utilizing adaptive mechanisms and their ability to learn. Such
systems should be capable of analyzing the spatio-temporal and user-specific
context of interaction, possibly with multiple partners.
(Link: http://www.uni-ulm.de/in/pit.html) |
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Sensor-based situation estimation and adaptive human-automotive
interaction in multi-vehicle scenarios
(Funding: State of Baden-Wuerttemberg)
The project aims at developing methods to evaluate the status, complexity and
potential danger of the current traffic situation on the basis of context
representations. Intentions and plans of other traffic participants will be
considered by integrating information gained from inter-vehicle communication
regarding the status of other vehicles. Information and behavioural strategies
for the driver-vehicle interaction will be derived and adapted accordingly.
The adaptive speech-based driver-vehicle interface will be triggered by the
complexity of the current traffic situation as well as the status and
attentiveness of the driver.
(Link: http://www.uni-ulm.de/in/pit.html) |
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Neural Decision-Making in Motion (Decisions-in Motion)
(Funding: European Union, EU FP6 IST Cognitive Systems integrated project, no. 027198)
The research goal of the project "DECISIONS-IN-MOTION" is to describe the
neural mechanisms used to guide behaviour in complex visual scenes, in which
the living (or animated) agent is in motion and navigates to avoid stationary
and/or moving objects. During reporting period P1 we have explored motion-based
image segmentation in the visual cortex, and we have begun to derive neural
models that explicitly make use of a hierarchy of sensory areas (low-, mid-,
high-level visual areas) to extract meaningful information about the location
and motion of objects in the environment. One objective of the project is to
use the outputs of these units for sensory-based decision-making. This process
will weight these inputs and relations between these inputs based on utility
functions. The resulting cognitive architecture will be tested in an autonomous
robot navigating in complex visual environments to determine the efficiency of
the image motion segmentation and goal-directed adaptive behaviour.
(Link: http://www.decisionsinmotion.org/) |
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Brain Plasticity and Perceptual Learning
(Funding: German Ministry of Research and Technology (BMBF), project no. 01GW0763)
This interdisciplinary project consortium will investigate cortical plasticity in humans to gain
better understanding of the neuronal changes and synaptic processes that take place during perceptual
learning. Compensatory alterations in brain function evoked by training in brain-damaged patients
will also be investigated. The sub-project Learning in the dorsal stream: Mechanisms and deficits
of complex motion processing (headed up by Neumann) will develop network models of the dorsal
pathway for areas MT and MST. The network properties during learning will be adjusted to fMRI data.
Different questions will be explored during the course of the project: (a) How learning will
influence local as well as global mechanisms in the representation of velocities at the different
stages of motion perception, (b) the role of velocity gradients and their explicit representation
during learning of motion discrimination, and (c) how learning can improve the resolution of motion
ambiguities in pattern motion configurations. The weight adaptation in the model will be based on
biologically plausible mechanisms of Hebbian plasticity.
(Link: http://www.brain-plasticity.org/) |
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Smart Eyes: Attending and Recognizing Instances of Salient Events (SEARISE)
(Funding: European Union, EU FP7 ICT project, no. 21 58 66-SEARISE)
The SEARISE project develops a trinocular active cognitive vision system, the Smart Eyes, for
detection, tracking and categorizaton of salient events and behaviors. Unlike other approaches
in video surveillance, the system will have human-like capability to learn continuously from the
visual input, self-adjust to ever changing visual environment, fixate salient events and follow
their motion, categorize salient events dependent on the context. Inspired by the human visual
system, a cyclopean camera will perform wide range monitoring of the visual field while active
binocular stereo cameras will fixate and track salient objects, mimicking a focus of attention
that switches between different interesting locations.
(Link: http://www.searise.eu/) |
