Aims / Research Topics

The aim of this strategic project is to gain insights into the complex questions of stability, robustness, solidity and efficiency of image feature extraction, aggregation and representation on the basis of mathematical modelling and analysis.

Research topics are in particular:

• Analysis of the stability and robustness of extraction and representation approaches taking into account stochastic resonance effects.
• Analysis of the costs of concepts for feature extraction and display in terms of energy consumption, bandwidth, memory and processing requirements.

Scientific Cooperation

• PhD cooperation with JKU-CP (Johannes Kepler University Linz / Department Computational Perception), topic Deep Learning
• PhD  cooperation with CTU-CMP (Czech Technical University in Pragu / Center for Machine Perception), topic Tracking in video sequences

Selected Publications

• Novel similarity and distance measures with applications in machine learning, image and signal processing.
• Riemannian manifold approach to scheimpflug camera calibration for embedded laser-camera application.
• Similarity recovery from threshold-based sampling under general conditions.
• On preserving metric properties of integrate-and-fire sampling.
• Matching event sequences approach based on Weyl's discrepancy norm.
• Stability of threshold-based sampling as metric problem

Funding Partners

This project is subsidized in the frame of COMET – Competence Centers for Excellent Technologies by BMK, BMDW, State of Upper Austria and its scientifc Partners. The COMET program is handeled by FFG.