Recherche

Fischer Andreas

Assoziierter Professor FH

Compétences principales

Assoziierter Professor FH

Téléphone: +41 26 429 67 34

Bureau: HEIA_D20.07

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH

BSc HES-SO en Informatique - Haute école d'ingénierie et d'architecture de Fribourg

Algorithms and Data Structures
Formal Languages
IT Project Management

En cours

Automatic handwriting recognition for tax form validator (2017-2020)

Rôle: Requérant(e) principal(e)

Financement: TAINA Technology

Description du projet :

2017-2020, TAINA Technology

Equipe de recherche au sein de la HES-SO: Fischer Andreas

Statut: En cours

Automatic translation from Swiss German to High German (2018-2021)

Rôle: Requérant(e) principal(e)

Financement: Swisscom

Description du projet :

2018-2021, Swisscom

Equipe de recherche au sein de la HES-SO: Fischer Andreas

Statut: En cours

Towards graph-based keyword spotting in historical Vietnamese steles (2020-2021)

Rôle: Requérant(e) principal(e)

Financement: Hasler Foundation

Description du projet :

2020-2021, Hasler Foundation

Equipe de recherche au sein de la HES-SO: Fischer Andreas

Statut: En cours

2021

The VCG tool :

Chapter ArODES

knowledge base and methods

Fischer Andreas, Keller Michael

AlpLinkBioEco (2021). Creating Bio-based Value in the Alpine Space. Interreg Alpine Space. (pp. 20-26). 2021, Interred Alpine Space : x

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Bio-based business opportunities unearthed :

Scientifique ArODES

the VCG software tool

Keller Michael, Fischer Andreas, Wessely Dorian, Mudaffer Ashna

Working Paper, February 2021, HES-SO//FR HEIA-FR, iCoSys, PICC, INNOSQUARE, Business Upper Austria,

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CITIZEN PARTICIPATION & DIGITAL TOOLS TO IMPROVE PEDESTRIAN MOBILITY IN CITIES

Scientifique

Romain Sandoz, Olivier Ertz, Anna Scius-Bertrand, Andreas Fischer, Olivier Hüsser, Hatem Ghorbel

The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 , vol. XLVI-4/W1-2021, pp. 29-34

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Résumé:

In this work, we present a framework supported by mobile and web apps and able to propose personalized pedestrian routes that match
user mobility profile considering mobility impediments factors. We explain how these later have been defined using a pedestrian-
centric approach based on travel experiences as perceived in the field by senior citizens. Through workshops, six main factors that may
influence pedestrian route choices were revealed: passability, obstacle in path, surface problem, security, sidewalk width, slope. These
categories were used to build digital tools and guide a citizen participatory approach to collect geolocated points of obstacle documented
with walkability information (picture, category, impact score, free comment). We also involved citizens to evaluate these information
and especially senior referents for validation. Finally we present how we connect these points of obstacle with a pedestrian network
based on OpenStreetMap to configure a routing cost function. The framework has been partially deployed in 2020 with limited people
due to the pandemic. Nonetheless, we share lessons learned from interaction with citizens in the design of such a framework whose
underlying workflow is reproducible. We plan to further assess its relevance and sustainability in the future.

2019

Combining graph edit distance and triplet networks for offline signature verification

Scientifique ArODES

Maergner Paul, Pondenkandath Vinaychandran, Alberti Michele, Liwicki Marcus, Riesen Kaspar, Ingold Rolf, Fischer Andreas

Pattern Recognition Letters, 2019, vol. 125, pp. 527-533

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Résumé:

Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.

2018

Keyword spotting in historical handwritten documents based on graph matching

Scientifique ArODES

Stauffer Michael, Riesen Kaspar, Fischer Andreas

Pattern Recognition, 2018, vol. 81 pp. 240-253

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Résumé:

In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.

Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance

Scientifique ArODES

Reza Ameri Mohammad, Stauffer Michael, Riesen Kaspar, Bui Tien D., Fischer Andreas

Pattern Recognition Letters,

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Résumé:

Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratictime approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.

Filters for graph-based keyword spotting in historical handwritten documents

Scientifique ArODES

Stauffer Michael, Fischer Andreas, Riesen Kaspar

Pattern Recognition Letters,

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Résumé:

The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions. Keyword Spotting (KWS), that allows to retrieve arbitrary query words from documents, has been proposed as alternative. In the present paper, we make use of graphs for representing word images. The actual keyword spotting is thus based on matching a query graph with all documents graphs. However, even with relative fast approximation algorithms the shear amount of matchings might limit the practical application of this approach. For this reason we present two novel filters with linear time complexity that allow to substantially reduce the number of graph matchings actually required. In particular, these filters estimate a graph dissimilarity between a query graph and all document graphs based on their node and edge distribution in a polar coordinate system. Eventually, all graphs from the document with distributions that differ to heavily from the query’s node/edge distribution are eliminated. In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected.

Dynamic signature verification system based on one real signature

Scientifique ArODES

Diaz Moises, Fischer Andreas, Ferrer Miguel A., Plamondon Réjean

IEEE Transactions on Cybernetics, 2018, vol. 48, no. 1, pp. 228-239

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Résumé:

The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.

2017

A user-centered segmentation method for complex historical manuscripts based on document graphs

Scientifique ArODES

Garz Angelika, Seuret Mathias, Fischer Andreas, Ingold Rolf

IEEE Transactions on Human-Machine Systems, 2017, vol. 47, no. 2, pp- 181-193

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Résumé:

In historical manuscripts, humans can detect handwritten words, lines, and decorations with lightness even if they do not know the language or the script. Yet for automatic processing this task has proven elusive, especially in the case of handwritten documents with complex layouts, which is why semiautomatic methods that integrate the human user into the process are needed. In this paper, we introduce a user-centered segmentation method based on document graphs and scribbling interaction. The graphs capture a sparse representation of the document's structure that can then be edited by the user with a stylus on a touch-sensitive screen. We evaluate the proposed method on a newly introduced database of historical manuscripts with complex layout and demonstrate, first, that the document graphs are already close to the desired segmentation and, second, that scribbling allows a natural and efficient interaction.

Signature verification based on the kinematic theory of rapid human movements

Scientifique ArODES

Fischer Andreas, Plamondon Réjean

IEEE Transactions on Human-Machine Systems, 207, vol. 47, no. 2, pp. 169-180

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Résumé:

When using tablet computers, smartphones, or digital pens, human users perform movements with a stylus or their fingers that can be analyzed by the kinematic theory of rapid human movements. In this paper, we present a user-centered system for signature verification that performs such a kinematic analysis to verify the identity of the user. It is one of the first systems that is based on a direct comparison of the elementary neuromuscular strokes which are detected in the handwriting. Taking into account the number of strokes, their similarity, and their timing, the string edit distance is employed to derive a dissimilarity measure for signature verification. On several benchmark datasets, we demonstrate that this neuromuscular analysis is complementary to a well-established verification using dynamic time warping. By combining both approaches, our verifier is able to outperform current state-of-the-art results in on-line signature verification.

Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignment

Scientifique ArODES

Fischer Andreas, Riesen Kaspar, Bunke Horst

Pattern Recognition Letters, 2017, vol. 87, no. 1, pp. 55-62

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Résumé:

Approximation of graph edit distance in polynomial time enables us to compare large, arbitrarily labeled graphs for structural pattern recognition. In a recent approximation framework, bipartite graph matching (BP) has been proposed to reduce the problem of edit distance to a cubic-time linear sum assignment problem (LSAP) between local substructures. Following the same line of research, first attempts towards quadratic-time approximation have been made recently, including a lower bound based on Hausdorff matching (Hausdorff Edit Distance) and an upper bound based on greedy assignment (Greedy Edit Distance). In this paper, we compare the two approaches and derive a novel upper bound (BP2) which combines advantages of both. In an experimental evaluation on the IAM graph database repository, we demonstrate that the proposed quadratic-time methods perform equally well or, quite surprisingly, in some cases even better than the cubic-time method.

2018

Offline signature verification by combining graph edit distance and triplet networks

Conference ArODES

Andreas Fischer

Proceedings of Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, 17-19 August 2018

Lien vers la publication

Résumé:

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.

Learning graph distances with message passing neural networks

Conference ArODES

Andreas Fischer

ICPR 2018, the 24th International Conference on Pattern Recognition, 20-24 August 2018, Beijing, China

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Résumé:

Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of errortolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks.

Offline signature verification via structural methods :

Conference ArODES

graph edit distance and inkball models

Andreas Fischer

Proceedings of ICFHR 2018, the 16th International Conference on Frontiers in Handwriting Recognition, 5-8 August 2018, Niagara Falls, USA

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Résumé:

For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.

Seamless GPU evaluation of smart expression templates

Conference ArODES

Jean Hennebert, Andreas Fischer

Proceedings of the 2018 International Conference on High Performance Computing & Simulation (HPCS 2018), The 16th Annual Meeting, 16-20 July 2018, Orléans, France

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Résumé:

Expression Templates is a technique allowing to write linear algebra code in C++ the same way it would be written on paper. It is also used extensively as a performance optimization technique, especially as the Smart Expression Templates form which allows for even higher performance. It has proved to be very efficient for computation on a Central Processing Unit (CPU). However, due to its design, it is not easily implemented on a Graphics Processing Unit (GPU). In this paper, we devise a set of techniques to allow the seamless evaluation of Smart Expression Templates on the GPU. The execution is transparent for the user of the library which still uses the matrices and vector as if it was on the CPU and profits from the performance and higher multi-processing capabilities of the GPU. We also show that the GPU version is significantly faster than the CPU version, without any change to the code of the user.

Extending the Sigma-Lognormal model of the kinematic theory to three dimensions

Conference ArODES

Andreas Fischer

Proceedings of ICPRAI 2018 - International Conference on Pattern Recognition and Artificial Intelligence, Celebrating the 30th Anniversary of CENPARMI, 14-17 May 2018 + Public Lecture on 13 May 2018, Concordia University, Montréal, Canada

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Résumé:

The Kinematic Theory of rapid human movements and its Sigma-Lognormal model enables to model human gestures, in particular complex handwriting patterns such as words, signatures and free gestures. This paper investigates the extension of the theory and its Sigma-Lognormal model from two dimensions to three, taking into account new acquisition modalities (motion capture), multiple subjects, and unconstrained motions. Despite the increased complexity and the new acquisition modalities, we demonstrate that the Sigma-Lognormal model can be successfully generalized to describe 3D human movements. Starting from the 2D model, we replace circular with spherical motions to derive a representation of unconstrained human movements with a new 3D Sigma-Lognormal model. First experiments show a high reconstruction quality with an average signal-tonoise ratio (SNR) of 18.52 dB on the HDM05 dataset. Gesture recognition using dynamic time warping (DTW) achieves similar recognition accuracies when using original and reconstructed gestures, which confirms the high quality of the proposed model.

Graph-based keyword spotting in historical documents using context-aware Hausdorff edit distance

Conference ArODES

Andreas Fischer

Proceedings of DAS 2018 : 13th IAPR International Workshop on Document Analysis Systems, 24-27 April 2018, Vienna, Austria

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Résumé:

Scanned handwritten historical documents are often not well accessible due to the limited feasibility of automatic full transcriptions. Thus, Keyword Spotting (KWS) has been proposed as an alternative to retrieve arbitrary query words from this kind of documents. In the present paper, word images are represented by means of graphs. That is, a graph is used to represent the inherent topological characteristics of handwriting. The actual keyword spotting is then based on matching a query graph with all document graphs. In particular, we make use of a fast graph matching algorithm that considers the contextual substructure of nodes. The motivation for this inclusion of node context is to increase the overall KWS accuracy. In an experimental evaluation on four historical documents, we show that the proposed procedure clearly outperforms diverse other template-based reference systems. Moreover, our novel framework keeps up or even outperforms many state-of-the-art learning-based KWS approaches.

2017

A structural approach to offline signature verification using graph edit distance

Conference ArODES

Andreas Fischer

Proceedings of 2017 14th IAPR International Conference on Document Analysis and Recognition, 9-15 November 2017, Kyoto, Japan

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Résumé:

Graphs provide a powerful representation formalism for handwritten signatures, capturing local properties as well as their relations. Yet, although introduced early for signature verification, only a few current systems rely on graph-based representations. A possible reason is the high computational complexity involved for matching two general graphs. In this paper, we introduce a novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance. We put forward several ways of creating, normalizing, and comparing signature graphs built from keypoints and investigate their performance on three benchmark datasets. The experiments demonstrate a promising performance of the proposed structural approach when compared with the state of the art.

Ensembles for graph-based keyword spotting in historical handwritten documents

Conference ArODES

Andreas Fischer

Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 9-15 November 2017, Kyoto, Japan

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Résumé:

Keyword Spotting (KWS) offers a convenient way to improve the accessibility to historical handwritten documents by retrieving search terms in scanned document images. The approach for KWS proposed in the present paper is based on segmented word images that are represented by means of different types of graphs. The actual keyword spotting is based on matching a query graph with a set of document graphs using the concept of graph edit distance. In particular, we propose to employ ensemble methods for KWS with graphs. That is, a query graph is not matched against one but several different graphs representing the same document word. Eventually, we use different strategies to combine these individual graph dissimilarities. In an experimental evaluation on two benchmark datasets, the proposed ensemble methods outperform the individual ensemble members as well as four state-of-the-art reference systems based on dynamic time warping.

Speeding-Up Graph-Based Keyword Spotting in Historical Handwritten Documents

Conference ArODES

Andreas Fischer

Lecture Notes in Computer Science ; Proceedings of International Workshop on Graph-based Representations in Pattern Recognition (GbRPR 2017), 16-18 May 2017, Anacapri, Italy

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Résumé:

The present paper is concerned with a graph-based system for Keyword Spotting (KWS) in historical documents. This particular system operates on segmented words that are in turn represented as graphs. The basic KWS process employs the cubic-time bipartite matching algorithm (BP). Yet, even though this graph matching procedure is relatively efficient, the computation time is a limiting factor for processing large volumes of historical manuscripts. In order to speed up our framework, we propose a novel fast rejection heuristic. This heuristic compares the node distribution of the query graph and the document graph in a polar coordinate system. This comparison can be accomplished in linear time. If the node distributions are similar enough, the BP matching is actually carried out (otherwise the document graph is rejected). In an experimental evaluation on two benchmark datasets we show that about 50% or more of the matchings can be omitted with this procedure while the KWS accuracy is not negatively affected.

A survey on applications of bipartite graph edit distance

Conference ArODES

Andreas Fischer

Lecture Notes in Computer Science ; Proceedings of International Workshop on Graph-based Representations in Pattern Recognition (GbRPR 2017), 16-18 May 2017, Anacapri, Italy

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Résumé:

About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. This particular framework allows the approximation of graph edit distance in cubic time. This, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. The present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).

Réalisations

Recherche

Fischer Andreas

Assoziierter Professor FH

Compétences principales

Pattern Recognition

Applied machine learning

Document Analysis

Handwriting Recognition

Natural Language Processing

Graph Matching

Geometric Deep Learning

Assoziierter Professor FH