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Dive into the research topics where Arne Grumpe is active.

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Featured researches published by Arne Grumpe.


international conference on image processing | 2011

Reconstruction of non-Lambertian surfaces by fusion of Shape from Shading and active range scanning

Steffen Herbort; Arne Grumpe; Christian Wöhler

In this paper, we present an algorithm for the fusion of surface normals estimated based on Shape from Shading with absolute depth data under exploitation of the mutual advantages, regarding non-Lambertian surfaces with non-uniform albedos. While photometric 3D reconstruction methods yield dense surface detail information which is reliable on small scales, active range scanning provides absolute depth data which are typically noisy on small scales but reliable on large scales. The proposed algorithm applies an iterative refinement to the reconstructed surface in order to suppress errors that result from measurement uncertainties in the surface normals and the absolute depth data by simultaneous minimization of a global error functional. The obtained surface is the best fit to the observed image intensities and depth data. We apply our framework to small-scale real-world objects and to regions of the lunar surface.


ieee international conference on image information processing | 2015

Extreme learning machine based novelty detection for incremental semi-supervised learning

Husam Al-Behadili; Arne Grumpe; Christian Dopp; Christian Wöhler

A variety of problems are related to streaming data e.g. infinite length, concept-drift, non-linearly separable classes, and the possible emergence of “novel classes”. We propose a semi-supervised learning method using an incremental neural network to cope with all these problems. Tracking the concept drift is maintained by using incremental learning. Additionally, the extreme value theory is used as a novelty detector technique to recognize outliers, since the semi-supervised learning is sensitive to them. The extreme learning machine is easily updated and it can be used for multiple classes. Superior properties are shown for the proposed algorithm as compared with an auto-encoder neural network. Particularly, the training time is greatly reduced hence it is adequate for online training.


european signal processing conference | 2017

Automatic crater detection and age estimation for mare regions on the lunar surface

Atheer L. Salih; Philipp Schulte; Arne Grumpe; Christian Wöhler; Harald Hiesinger

In this paper, we investigate how well an automatic crater detection algorithm is suitable to determine the surface age of different lunar regions. A template-based crater detection algorithm is used to analyze image data under known illumination conditions. For this purpose, artificially illuminated crater templates are used to detect and count craters and their diameters in the areas under investigation. The automatic detection results are used to obtain the crater size-frequency distribution (CSFD) for the examined areas, which is then used for estimating the absolute model age (AMA) of the surface. The main focus of this work is to find out whether there exists an ideal sensitivity value for automatic crater detection to obtain smallest possible errors between the automatically derived AMA and a reference AMA derived from manually detected craters. The detection sensitivity threshold of our crater detection algorithm (CDA) is calibrated based on five different regions in Mare Cognitum on the Moon such that the age inferred from the manual crater counts corresponds to the age inferred from the CDA results. The obtained best detection threshold value is used to apply the CDA algorithm to another five regions in the lunar Oceanus Procellarum region. The accuracy of the method is examined by comparing the calculated AMAs with the manually determined ones from the literature. It is shown that the automatic age estimation yields AMA values that are generally consistent with the reference values with respect to the one standard deviation errors.


ieee international conference on progress in informatics and computing | 2015

Non-linear distance based large scale data classifications

Husam Al-Behadili; Arne Grumpe; Christian Dopp; Christian Wöhler

Linear subspace projections are an important technique to reduce the dimensionality of data for automatic classification. Especially for large-scale and on-line systems, e.g. gesture recognition applications, this is important to guarantee near real-time processing. The linear subspace projections, however, fail if the classes are not linearly separable. Kernel methods, in contrast, have been widely applied to linear classification algorithms to solve problems of non-linearly separable classes. This technique, however, increases the computational complexity by introducing the evaluation of a possibly non-linear function. Here, we extend a linear subspace projection that has been applied to large-scale systems using a kernel function. The method is evaluated on Fishers Iris dataset and a recorded gesture dataset. The results indicate that the proposed method yields an increased accuracy at a subspace of lower dimension while achieving a similar runtime at a subspace of the same dimension. The proposed method is thus expected to work well with online systems.


2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS) | 2015

Incremental learning and novelty detection of gestures using extreme value theory

Husam Al-Behadili; Arne Grumpe; Christian Dopp; Christian Wöhler

The problems of data streaming, e.g. infinite length and concept-drift, require incremental self-adapting classifiers. The performance of the classifier, however, is affected by false labels. Consequently, the classifier is required to detect outliers or samples belonging to unseen classes, i.e. novelties. We propose an incremental Mahalanobis distance based classifier using extreme value theory to detect novelties. Extreme value theory allows for the determination of a global constant threshold that does not change during the adaption of the classifier and thus does not need additional validation data and/or procedures. The results show high accuracy and high efficiency in linear and non-linear spaces with respect to recognition results and computation time.


2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA) | 2011

DEM construction and calibration of hyperspectral image data using pairs of radiance images

Arne Grumpe; Christian Wöhler


Isprs Journal of Photogrammetry and Remote Sensing | 2017

Construction of pixel-level resolution DEMs from monocular images by shape and albedo from shading constrained with low-resolution DEM

Bo Wu; Wai Chung Liu; Arne Grumpe; Christian Wöhler


Icarus | 2017

Temperature regime and water/hydroxyl behavior in the crater Boguslawsky on the Moon

Christian Wöhler; Arne Grumpe; A.A. Berezhnoy; Ekaterina A. Feoktistova; Nadezhda A. Evdokimova; Karan Kapoor; Vladislav Shevchenko


Icarus | 2015

A comparative study of iron abundance estimation methods: Application to the western nearside of the Moon

Megha Upendra Bhatt; U. Mall; Christian Wöhler; Arne Grumpe; Roberto Bugiolacchi


ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | 2016

MAPPING OF PLANETARY SURFACE AGE BASED ON CRATER STATISTICS OBTAINED BY AN AUTOMATIC DETECTION ALGORITHM

Atheer L. Salih; M. Mühlbauer; Arne Grumpe; J. H. Pasckert; Christian Wöhler; H. Hiesinger

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Christian Wöhler

Technical University of Dortmund

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Bo Wu

Hong Kong Polytechnic University

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Wai Chung Liu

Hong Kong Polytechnic University

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