Bio-inspired Machine Vision
I received an M.S. from the University of Technology, Graz and a Ph.D. from the Technical University of Vienna, Austria in Applied Mathematics. I am Associate Research Scientist at the
Center for Automation Research at the Institute for Advanced Computer Studies at UMD. I cofounded the
Autonomy Cognition and Robotics (ARC) Lab and co-lead the
Perception and Robotics Group at UMD. I am the PI of an NSF sponsored Science of Learning Center Network for Neuromporphic Engineering and co-organize the
Neuromorphic Engineering and Cognition Workshop.
My research is in the areas of Computer Vision, Robotics, and Human Vision, focusing on biological-inspired solutions for active vision systems. I have modeled perception problems using tools from geometry, statistics and signal processing and developed software in the areas of multiple view geometry, motion, navigation, shape, texture, and action recognition. I have also combined computational modeling with psychophysical experiments to gain insights into human motion and low-level feature perception.
My current work is on robot vision in the following two areas:
1) Integrating perception, action, and high-level reasoning to interpret human manipulation actions with the ultimate goal of advancing collaborative robots and creating robots that visually learn from humans.
2) Motion processing for fast active robots (such as drones) using as input bio-inspired event-based sensors.
By integrating cognitive processes with perception and action execution, we investigate ways of structuring representations of events at multiple time spans, that allow generalization of actions. The main application of this work is to create robots that visually learn from humans.Read more
Dynamic vision sensors, because of their high temporal resolution, low latency, high dynamic range, and high compression, hold promises for autonomous Robotics. We study the advantages of this data for fundamental navigation processes of egomotion, segmentation. and image motion.Read more
Between low-level image processing and high-level reasoning are grouping mechanism that implement principles of Gestalt, such as closure, or symmetry. We have developed new 2D image-processing operators and 3D operators that implemet these principles for attention, segmentation and recognition.Read more
Active vision system compute from video essential information about their environment's spatio-temporal geometry for navigation. In a series of studies, I investigated the recovery 3D motion and scene structure from image motion and implementation in efficient algorithms.Read more
Taking advantage of the mathematical properties of fractal geometry, we designed texture descriptors that are inavriant to changes in viewpoint, geometric deformation , and illumination, and which encode with very low dimension the essential structure of textures.Read more
Optical illusions can provide insight into the mechanism of vision. Using geometry and statistics I have uncovered a number of principles explaining limitations in what we can recover from images, and these principles can explain different optical illusions and were used to create new ones.Read more
July 2019: I co-organized a project on "Machine Common Sense: An Embodied and Developmental Paradigm"
at the Telluride Neuromorphic Cognition Workshop 2019.
June 2019: Seedgrant award from the Maryland Robotics Center supporting a PostDoc fellowship
May 2019: Provost's Excellence Award for Professional Track Faculty
February 2019: UMD-Northrop Grumman seedgrant on "Co-design of Structure and Intelligence for Embedded System Optimization"
August 2018: NSF award from the Science of Learning
Title: Research Coordination Network: Cognitive Functions in the Learning of Symbolic Signals and Systems
March 2018: My work on the Cyberphysical grant on Monitoring Humans was featured in a special issue in
Research Features on Women's day.
November 2019: The paper EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
will be presented at IROS 2019."
August 2019: Our paper on Topology-Aware Non-Rigid Point Cloud Registration,
was accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence.
June 2019: Invited lecture at Second International Workshop on Event-based Vision and Smart Cameras (CVPR'19).
May 2019: Our paper on Learning sensorimotor control with
neuromorphic sensors: Toward hyperdimensional active perception,
was accepted at Science Robotics.
Currently I serve for the folllowing journals and conferences
email — email@example.com
phone — 301 405 1768
office — Iribe Building 4216
ADDRESS (for shipping)
Computer Vision Lab, UMIACS
5109 Brendan Iribe Center for Computer Science and Engineering
8125 Paint Branch Dr, College Park, MD 20742