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Introduction
Publications
Publications (23)
Helmholtz AI at FZJ addresses the current transformation of science regarding different aspects of digitization especially at the overlap of AI with high-performance computing (HPC), and neuroscience. The unit is built on the long and intense interdisciplinary partnership between Juelich Supercomputing Centre (JSC) and Institute of Neuroscience and...
Streets are essential entities of urban terrain and their automatic extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological, and semantic aspects. Given a binary image representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies...
The estimation of land cover fractions from remote sensing images is a frequently used indicator of the environmental quality. This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral EnMAP data with a spatial resolution of 30m$\times$30m. We use constrained sparse represent...
Global climate change plays an essential role in our daily life and is nowadays one of the most important topics. Mesoscale ocean eddies have a significant impact on global warming, since they dominate the ocean dynamics, the energy as well as the mass transports of ocean circulation. In particular, from satellite altimetry we can derive high-resol...
This paper addresses the land cover classification task for remote sensing images by deep self-taught learning. Our self-taught learning approach learns suitable feature representations of the input data using sparse representation and undercomplete dictionary learning. We propose a deep learning framework which extracts representations in multiple...
Sea level change, one of the most dire impacts of anthropogenic global warming, will affect a large amount of the world's population. However, sea level change is not uniform in time and space, and the skill of conventional prediction methods is limited due to the ocean's internal variabi-lity on timescales from weeks to decades. Here we study the...
We propose sparse representation-based archetypal graphs as input to spectral clustering for anomaly and change detection. The graph consists of vertices defined by data samples and edges which weights are determines by sparse representation. Besides relationships between all data samples, the graph also encodes the relationship to extremal points,...
Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a
complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road
segments are extracted by means of skeletonization. The focus of this paper lie...
Self-taught learning (STL) has become a promising paradigm to exploit unlabeled data for classification. The most commonly used approach to self-taught learning is sparse representation, in which it is assumed that each sample can be represented by a weighted linear combination of elements of a unlabeled dictionary. This paper proposes discriminati...
The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more
fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the
analysis of latent traces and extends the classical concept to the conservation o...
Our objective is the interpretation of facade images in a top-down manner, using a Markov marked point process formulated as a Gibbs process. Given single rectified facade images, we aim at the accurate detection of relevant facade objects as windows and entrances, using prior knowledge about their possible configurations within facade images. We r...
Our objective is the interpretation of facade images in a top-down manner, using a Markov marked point process formulated as a Gibbs process. Given single rectified facade images, we aim at the accurate detection of relevant facade objects as windows and entrances, using prior knowledge about their possible configurations within facade images. We r...
Simplification of given polygons has attracted many researchers. Especially, finding circular and elliptical structures in images is relevant in many applications. Given pixel chains from edge detection, this paper proposes a method to segment them into straight line and ellipse segments. We propose an adaption of Douglas-Peucker's polygon simplifi...
Our objective is the categorization of the most dominant objects in facade images, like windows, entrances and balconies. In order
to execute an image interpretation of complex scenes we need an interaction between low level bottom-up feature detection and highlevel
inference from top-down. A top-down approach would use results of a bottom-up detec...
In this paper, we point out the role of sequences of samples for training an incremental learning method.
We define characteristics of incremental learning methods to describe the influence of sample ordering on the performance of a learned model.
We show the influence of sequence for two different types of incremental learning.
One is aimed on le...
We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of
their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of
facades. No explicit notion of a window is used; thus, the method also appears to be able to identify oth...
We propose an incremental learning scheme for learning a class hierarchy for objects typically occurring multiple in images. Given one example of an object that appears several times in the image, e.g. is part of a repetitive structure, we propose a method for identifying prototypes using an unsupervised clustering procedure. These prototypes are u...
We present a method for detecting repeated structures, which is applied on facade images for describing the regularity of their windows. Our approach finds and explicitly represents repetitive structures and thus gives initial representation of facades. No explicit notion of a window is used, thus the method also appears to be able to identify othe...
Zusammenfassung: Regelmäßige Strukturen und Symmetrien kennzeichnen viele Gebäudefassaden oder Objekte im Umfeld von Gebäuden. Für die automatisierte Bildinterpretation weisen diese Strukturen auf künstliche Objekte hin, führen aber auch zu Schwierigkeiten bei klassischen Bildzuordnungsverfahren. Die Suche und Gruppierung zusammengehöriger Merkmale...
