Matthias O. Franz

• Dr. rer. nat.
• Professor at HTWG Konstanz - University of Applied Sciences

There is a palpable shift in mainstream attitude towards geoengineering technologies, seen now as potential parts of a climate policy mix. Still, concerning solar radiation management (SRM) in particular, because of the known and unknown undesirable side-effects of various engineering implementations of theirs, it is important to know what is the m...

Side effects of solar radiation management geoengineering are unavoidable because the forcing involved is different in nature from the anthropogenic greenhouse forcing. Yet, the side effects should scale with the magnitude of the geonegineering forcing. Even then it is crucial to have detailed information about them. We use a response theory based...

It is common to speak about side effects of solar radiation management geoengineering considering that the forcing involved is different in nature from the anthropogenic greenhouse forcing. However, we argue that even if side effects are identified in terms of uncontrolled changes of climatic variables, they should scale with the geoengineering for...

It is common to speak about side effects of solar radiation management geoengineering considering that the forcing involved is different in nature from the anthropogenic greenhouse forcing. However, we argue that even if side effects are identified in terms of uncontrolled changes of climatic variables, they should scale with the geoengineering for...

There is a palpable shift in mainstream attitude towards geoengineering, seen now as a potential part of a climate policy mix. Still, no-one wants to get on a slippery slope, compounding the risks, and, therefore, we should ask ourselves what is the minimal geoengineering that we can get away with. Such questions lead mathematically to inverse prob...

There is a palpable shift in mainstream attitude towards geoengineering, seen now as a potential part of a climate policy mix. Still, no-one wants to get on a slippery slope, compounding the risks, and, therefore, we should ask ourselves what is the minimal geoengineering that we can get away with. Such questions lead mathematically to inverse prob...

There is a palpable shift in mainstream attitude towards geoengineering technologies, seen now as potential parts of a climate policy mix. Still, concerning solar radiation management (SRM) in particular, because of the known and unknown undesirable side-effects of various engineering implementations of theirs, it is important to know what is the m...

There is a palpable shift in mainstream attitude towards geoengineering technologies, seen now as potential parts of a climate policy mix. Still, concerning solar radiation management (SRM) in particular, because of the known and unknown undesirable side-effects of various engineering implementations of theirs, it is important to know what is the m...

Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our approach first transforms the training and test images into ensembles of patch...

The detection of anomalous or novel images given a training dataset of only clean reference data (inliers) is an important task in computer vision. We propose a new shallow approach that represents both inlier and outlier images as ensembles of patches, which allows us to effectively detect novelties as mean shifts between reference data and outlie...

We are concerned with nonparametric hypothesis testing of time series functionals. It is known that the popular autoregressive sieve bootstrap is, in general, not valid for statistics whose (asymptotic) distribution depends on moments of order higher than two, irrespective of whether the data come from a linear time series or a nonlinear one. Inspi...

Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate chan...

Multi-Dimensional Connectionist Classification is a method for weakly supervised training of Deep Neural Networks for segmentation-free multi-line offline handwriting recognition. MDCC applies Conditional Random Fields as an alignment function for this task. We discuss the structure and patterns of handwritten text that can be used for building a C...

Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-paramete...

In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to incl...

In this paper we present a method using deep learning to compute parametrizations for B-spline curve approximation. Existing methods consider the computation of parametric values and a knot vector as separate problems. We propose to train interdependent deep neural networks to predict parametric values and knots. We show that it is possible to incl...

In inspection systems for textured surfaces, a reference texture is typically known before novel examples are inspected. Mostly, the reference is only available in a digital format. As a consequence, there is no dataset of defective examples available that could be used to train a classifier. We propose a texture model approach to novelty detection...

Offline handwriting recognition systems often use LSTM networks, trained with line-or word-images. Multi-line text makes it necessary to use segmentation to explicitly obtain these images. Skewed, curved, overlapping, incorrectly written text, or noise can lead to errors during segmentation of multi-line text and reduces the overall recognition cap...

Algorithms for calculating the string edit distance are used in e.g. information retrieval and document analysis systems or for evaluation of text recognizers. Text recognition based on CTC-trained LSTM networks includes a decoding step to produce a string, possibly using a language model, and evaluation using the string edit distance. The decoded...

Knot placement for curve approximation is a well known and yet open problem in geometric modeling. Selecting knot values that yield good approximations is a challenging task, based largely on heuristics and user experience. More advanced approaches range from parametric averaging to genetic algorithms. In this paper, we propose to use Support Vecto...

Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) for image inpainting of large regions in high-resolution textures. Due to limited computational resources processing high-resoluti...

In the reverse engineering process one has to classify parts of point clouds with the correct type of geometric primitive. Features based on different geometric properties like point relations, normals, and curvature information can be used to train classifiers like Support Vector Machines (SVM). These geometric features are estimated in the local...

Recent years have seen the proposal of several different gradient-based optimization methods for training artificial neural networks. Traditional methods include steepest descent with momentum, newer methods are based on per-parameter learning rates and some approximate Newton-step updates. This work contains the result of several experiments compa...

The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regularize...

Digital cameras are used in a large variety of scientific and industrial applications. For most applications the acquired data should represent the real light intensity per pixel as accurately as possible. However, digital cameras are subject to different sources of noise which distort the resulting image. Noise includes photon noise, fixed pattern...

Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwritin...

The detection of differences between images of a printed reference and a reprinted wood decor often requires an initial image registration step. Depending on the digitalization method, the reprint will be displaced and rotated with respect to the reference. The aim of registration is to match the images as precisely as possible. In our approach, im...

Classification of point clouds by different types of geometric primitives is an essential part in the reconstruction process of CAD geometry. We use support vector machines (SVM) to label patches in point clouds with the class labels tori, ellipsoids, spheres, cones, cylinders or planes. For the classification, features based on different geometric...

Digital cameras are subject to physical, electronic and optic effects that result in errors and noise in the image. These effects include for example a temperature dependent dark current, read noise, optical vignetting or different sensitivities of individual pixels. The task of a radiometric calibration is to reduce these errors in the image and t...

Atom interferometers have a multitude of proposed applications in space including precise measurements of the Earth's gravitational field, in navigation & ranging, and in fundamental physics such as tests of the weak equivalence principle (WEP) and gravitational wave detection. While atom interferometers are realized routinely in ground-based labor...

The theory of general relativity describes macroscopic phenomena driven by the influence of gravity while quantum mechanics brilliantly accounts for microscopic effects. Despite their tremendous individual success, a complete unification of fundamental interactions is missing and remains one of the most challenging and important quests in modern th...

We present the development of compact and ruggedized iodine-based frequency references on elegant breadboard (EBB) and engineering model (EM) level using modulation transfer spectroscopy near 532 nm. A frequency stabilty of 1�10

Watermarked images are increasingly prevalent in the internet. Hence, any practical steganalyzer has to take the presence of watermarked images into account, particularly as potential source of false alarms due to similar embedding algorithms. In this study, we investigate the impact of watermarked images on the performance of a standard steganalyz...

We present the development of a space-compatible laser frequency stabilization to a hyperfine transition in molecular iodine with a frequency stability in the 10-15 domain at longer integration times. A setup on elegant breadboard (EBB) level was successfully implemented and verified. Using modulation-transfer spectroscopy, a frequency stability of...

Today, support vector machines (SVMs) seem to be the classifier of choice in blind steganalysis. This approach needs two steps: first, a training phase determines a separating hyperplane that distinguishes between cover and stego images; second, in a test phase the class membership of an unknown input image is detected using this hyperplane. As in...

This paper presents a mapping approach for inland waters using a noisy radar sensor installed on a boat. The vessel’s position is acquired from GPS, thus this is a pure mapping problem. For the actual mapping the probabilistic open-source mapping framework octomap as presented by [WHB+10] is used. Exactly one polygon is extracted from a binary rada...

Identification of stimulus-response functions is a central problem in systems neuroscience and related areas. Prominent examples are the estimation of receptive fields and classification images [1]. In most cases, the relationship between a high-dimensional input and the system output is modeled by a linear (first-order) or quadratic (second-order)...

where the Volterra kernel is given as a finite number of mⁿ coefficients h⁽ⁿ⁾i1 ... in. It is, accordingly, a linear combination of all ordered nth-order monomials of the components of the input vector x. The discretized Volterra functionals provide a practical approximation which shares the completeness and convergence properties of Volterra theor...

Zusammenfassung Fledermäuse können anhand von Ultraschallsignalen sehr komplexe Unterscheidungen treffen. Bisher ist es immer noch unklar, welche Signalmerkmale die Grundlage für die erstaunlichen Verhaltensleistungen dieser Tiere bilden. Anhand zweier Beispiele, der Bestimmung der Artzugehörigkeit einer Pflanze aus ihrem Ultraschallecho und der Er...

Multi-camera systems and GPU-based stereo-matching methods allow for a real-time 3d reconstruction of faces. We use the data generated by such a 3d reconstruction for a hybrid face recognition system based on color, accuracy, and depth information. This system is structured in two subsequent phases: geometry-based data preparation and face recognit...

Echo-locating bats constantly emit ultrasonic pulses and analyze the returning echoes to detect, localize, and classify objects in their surroundings. Echo classification is essential for bats' everyday life; for instance, it enables bats to use acoustical landmarks for navigation and to recognize food sources from other objects. Most of the resear...

Most universal steganalysis techniques use an image model to reconstruct an estimate of the original, unmanipulated cover from the input. Differences between reconstructed and input images are an indication of a steganographic manipulation. In this paper, we analyze the relation between the modeling error of the image model and detection performanc...

Most current algorithms for blind steganalysis of images are based on a two-stages approach: First, features are extracted in order to reduce dimensionality and to highlight potential manipulations; second, a classifier trained on pairs of clean and stego images finds a decision rule for these features to detect stego images. Thereby, vector compon...

For simple visual patterns under the experimenter's control we impose which information, or features, an observer can use to solve a given perceptual task. For natural vision tasks, however, there are typically a multitude of potential features in a given visual scene which the visual system may be exploiting when analyzing it: edges, corners, cont...

We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interest...

Imitation learning is a promising technique for teach- ing robots complex movement sequences. One key problem in this area is the transfer of perceived movement charac- teristics from perception to action. For the solution of this problem, representations are required that are suitable for the analysis and the synthesis of complex action sequences....

Humans perceives the world by directing the center of gaze from one location to another via rapid eye movements, called saccades. In the period between saccades the direction of gaze is held fixed for a few hundred milliseconds (fixations). It is primarily during fixations that information enters the visual system. Remarkably, however, after only a...

We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the ima...

Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of non-linearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether qua...

A critical step on the way to understanding a sensory system is the analysis of the input it receives. In this work we examine the statistics of natural complex echoes, focusing on vegetation echoes. Vegetation echoes constitute a major part of the sensory world of more than 800 species of echolocating bats and play an important role in several of...

Echolocating bats use the echoes from their echolocation calls to perceive their surroundings. The ability to use these continuously emitted calls, whose main function is not communication, for recognition of individual conspecifics might facilitate many of the social behaviours observed in bats. Several studies of individual-specific information i...

The human visual system is foveated, that is, outside the central visual field resolution and acuity drop rapidly. Nonetheless much of a visual scene is perceived after only a few saccadic eye movements, suggesting an effective strategy for selecting saccade targets. It has been known for some time that local image structure at saccade targets infl...

To explore the statistics of complex natural plant echoes, we emitted bat-like downsweeps (200-0 kHz) and recorded the echoes of various tree species. A Hilbert transform was used to calculate the envelope of the echoes impulse responses. This corresponds to a one-dimensional representation of the spatial reflector arrangement of the plant. We then...

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the e...

Complex navigation behaviour (way-finding) involves recognizing several places and encoding a spatial relationship between them. Way-finding skills can be classified into a hierarchy according to the complexity of the tasks that can be performed [8]. The most basic form of way-finding is route navigation, followed by topological navigation where se...

Interest point detection in still images is a well-studied topic in computer vision. In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by emph{learning} a detector from examples: we record eye movements of human subjects watching video sequences and train...

Papers from the 2006 flagship meeting on neural computation, with contributions from physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. It draws a diverse group of attendees—physici...

Volterra and Wiener series are perhaps the best-understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estim...

We present an approach for designing interest operators that are based on human eye movement statistics. In contrast to existing methods which use hand-crafted saliency measures, we use machine learning methods to infer an interest operator directly from eye movement data. That way, the operator provides a measure of biologically plausible interest...

In this paper, we use large neighborhood Markov random fields to learn rich prior models of color images. Our approach ex- tends the monochromatic Fields of Experts model (Roth & Black, 2005a) to color images. In the Fields of Experts model, the curse of dimensionality due to very large clique sizes is circumvented by parameterizing the poten- tial...

This paper addresses the bottom-up influence of local image i nformation on hu- man eye movements. Most existing computational models use a set of biolog- ically plausible linear filters, e.g., Gabor or Difference- of-Gaussians filters as a front-end, the outputs of which are nonlinearly combined into a real number that indicates visual saliency. U...

In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We ther...

The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities, and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regulariz...

This paper proposes a method for computing fast approximations to support vector decision functions in the field of object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller, so-called reduced set of synthesized input space points. In contrast to the existing method...

Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consis...

The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this study, we investigate an alternative regularization technique that results in an implicit whitening of the basis functions by penalizing directions in function space with a large prior variance. The regularizatio...

We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1.~The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2.~A generic machine learning appro...

We present a new approximation scheme for support vector decision functions in object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller so-called reduced set of synthetic points. Instead of finding the reduced set via unconstrained optimization, we impose a structur...

We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss fun...

We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2. A generic machine learning appro...

The computation of classical higher-order statistics such as higher-order moments or spectra is difficult for images due to the huge number of terms to be estimated and interpreted. We propose an alternative approach in which multiplicative pixel interactions are described by a series of Wiener functionals. Since the functionals are estimated impli...

The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevents its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method base...

This paper presents a method for single-frame image super-resolution using an unsupervised learning technique. The required prior knowledge about the high-resolution images is obtained from Kernel Principal Component Analysis (KPCA). The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited numb...

The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose an implicit estimation method based...

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of op...

The Wiener series is one of the standard methods to systematically characterize the nonlinearity of a neural system. The classical estimation method of the expansion coefficients via cross-correlation suffers from severe problems that prevent its application to high-dimensional and strongly nonlinear systems. We propose a new estimation method base...

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowl...

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowl...

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to esti- mate self-motion from the optic flow. We present a theory for the con- struction of an estimator consisting of a linear combination o...

In this chapter we review two pieces of work aimed at understanding the principal limits of extracting egomotion parameters from optic flow fields (Dahmen et al. 1997) and the functional significance of the receptive field organization of motion sensitive neurones in the fly’s visual system (Franz and Krapp 1999). In the first study, we simulated n...

The receptive field organization of a class of visual interneurons in the fly brain (vertical system, or VS neurons) shows a striking similarity to certain self-motion-induced optic flow fields. The present study compares the measured motion sensitivities of the VS neurons (Krapp et al. 1998) to a matched filter model for optic flow fields generate...

In visual homing tasks, animals as well as robots can compute their movements from the current view and a snapshot taken at a home position. Solving this problem exactly would require knowledge about the distances to visible landmarks, information, which is not directly available to passive vision systems. We propose a homing scheme that dispenses...

In the past decade, a large number of robots have been built that explicitly implement biological navigation behaviours. We review these biomimetic approaches using a framework that allows for a common description of biological and technical navigation behaviour. The review shows that biomimetic systems make significant contributions to two fields...

In the past decade, a large number of robots has been built that explicitly implement biological navigation behaviours. We review these biomimetic approaches using a framework that allows for a common description of biological and technical navigation behaviour. The review shows that biomimetic systems make significant contributions to two fields o...