Roland Mörzinger

École Polytechnique Fédérale de Lausanne

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Georg Thallinger

Joanneum Research

Peter Schallauer

Joanneum Research

René Schuster

German Research Centre for Artificial Intelligence

Werner Bailer

Joanneum Research

Allan Hanbury

Complexity Science Hub Vienna

Marcus Thaler

Joanneum Research

Robert Sorschag

TU Wien

Irena Sailer

University of Geneva

Helmut Gräbner

ZHAW Zurich University of Applied Sciences

Mihai Lupu

Bucharest University of Economic Studies

Cooperative Institutions

Faculty of 1000 (United States)

Joanneum Research

John Wiley & Sons (United States)

Dublin City University

École Polytechnique Fédérale de Lausanne

University of Southampton

ETH Zurich

National Technical University of Athens

National Medical Research Center of Dentistry and Maxillofacial Surgery

University Medical Center Groningen

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Automatic Image Annotation using Visual Content and Folksonomies

Roland Mörzinger Robert Sorschag Georg Thallinger Stefanie Lindstaedt

Content-Based Image Retrieval

Content (measure theory)

10.5220/0002337600580066

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Citations (3)

Patent images - a glass-encased tool

Mihai Lupu René Schuster Roland Mörzinger Florina Piroi Tobias Schleser

The paper discusses the problem of patent image retrieval. It describes the issues faced when extracting semantic data of images in patents, as well as an integration framework between the data thus extracted and semantic information extracted from text. Combining the two sources of knowledge is on the wish list of many patent information users, as current systems either search only the textual data, or have extremely limited image processing functionality. In practice in the patent domain, depictions of the product or method are often vital to the understanding of the invention. Yet they are almost completely unsearchable. They are tools enclosed in a glass case, at which we can look, but of which we cannot really make use. The IMPEx Project (Image Mining for Patent Exploration) cracks open this case with a new focus on processing this particular type of images. This paper presents the motivations, status and aims of the project.

Patent visualisation

10.1145/2362456.2362477

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Patent image retrieval

Allan Hanbury Naeem Bhatti Mihai Lupu Roland Mörzinger

Drawings are an important component of patents, and many search tasks in the intellectual property domain rely on the comparison of patent drawings. In this paper, we begin with a review of algorithms developed for the automated retrieval of similar images in the patent domain. There is however a larger body of research dedicated to analysis and retrieval of images found in technical documents that is also applicable to the images found in patents. We present an overview of this research for technical drawings, diagrams, charts, plots and chemical structures. Finally, we discuss the evaluation of image retrieval for patents, including short descriptions of the new patent image evaluation tasks in the CLEF-IP and TREC-CHEM evaluation campaigns in 2011.

Clef

10.1145/2064975.2064979

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Citations (27)

Automatic region of interest detection in tagged images

Robert Sorschag Roland Mörzinger Georg Thallinger

On the Web, tagging is the preferred approach to describing multimedia items in order to make them searchable. The information value of tags can be significantly enhanced if they are linked to specific image regions. In this paper, we describe an approach to automatically detect regions of interest (ROIs) that are visually related to a given tag. Our technique is domain independent and works unsupervised, just by leveraging the knowledge from large-scale collections of tagged images. The ROIs are obtained by local feature matching between similarly tagged images. We demonstrate the performance and high accuracy of our approach in experiments on a set of 41 different topics and more than 9000 images.

Region of interest

Feature (linguistics)

10.1109/icme.2009.5202827

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Citations (4)

RapidandReliable Detection ofFilmGrainNoise

Peter Schallauer Roland Mörzinger

Theknowledge ofmaterial specific filmgrain characteristics can significantly improve theperformance ofdigital filmprocessing algorithms. Thispaper proposes arapid andreliable detector for filmgrainproperties. First, homogeneous blocksconcerning intensity andtexture aredetermined byanovel measure calculated inthefrequency domain. Second, based onthese blocks thesignaldependent grain noise level isestimated andanimageregion of purefilmgrain isdetected. Evaluation based onawiderangeof structured images withhighfrequent electronic noiseandlow frequent filmgrain, showsthat themethod performs withaworst caseestimation error of2.54dBintypical moviequality (PSNRof 25-40 dB).Thismeets therequirements ofreal applications suchas digital film restoration andspecial effects compositing. IndexTerms-randomnoise, signal dependency, homogeneous textures, digital film restoration 1.INTRODUCTIONAND RELATED WORK

Compositing

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Comparison of user satisfaction and image quality of fixed and mobile camera systems for 3-dimensional image capture of edentulous patients: A pilot clinical study

Journal of Prosthetic Dentistry (2018)

Irena Sailer Shiming Liu Roland Mörzinger Marcel Lancelle Thabo Beeler

10.1016/j.prosdent.2018.04.005

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Citations (8)

TRECVid 2008 High Level Feature Extraction Experiments at JOANNEUM RESEARCH.

TRECVID (2008)

Roland Mörzinger Georg Thallinger

This paper describes our experiments for the high level feature extraction task in TRECVid 2008. We submitted the following six runs: • A jrs1 1: Baseline, early fusion, tuned SVMs. • A jrs2 2: Early fusion, SVMs without parameter tuning. • A jrs3 3: Late fusion, tuned SVMs. • A jrs4 4: Early fusion, transductive SVM (TSVM) learning using UniverSVM with unlabeled samples on the scale of 20% of test set size. • A jrs5 5: Early fusion, transductive SVM learning using SVM TSVM with unlabeled samples on the scale of the training set size. • A jrs6 6: Early fusion, transductive SVM learning using SVM TSVM with unlabeled samples on the scale of 20% of test set size. The experiments were designed to study both the performance of various content-based features in connection with classic early and late feature fusion as well as the influence of SVM parameter tuning and learning from unlabeled test data with different implementations of transductive support vector machines (TSVMs). The results show that the time-consuming parameter tuning improves precision only marginally. Compared to standard SVMs, TSVMs did not provide overall improvement and only slight benefits for concepts with a small number of training samples.

Feature (linguistics)

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A framework for unsupervised mesh based segmentation of moving objects

Multimedia Tools and Applications (2009)

Andreas Kriechbaum Roland Mörzinger Georg Thallinger

Ground truth

Feature (linguistics)

Representation

10.1007/s11042-009-0366-9

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Citations (3)

Automatic Quality Analysis for Film and Video Restoration

Peter Schallauer Werner Bailer Roland Mörzinger Hermann Fürntratt Georg Thallinger

A considerable amount of work in larger film and video restoration projects is dedicated to manually exploring the audiovisual content in order to estimate the costs for restoration and to plan the restoration. Manual exploration is a significant cost factor. In this paper we propose automatic content analysis algorithms and summarization techniques which allow the reduction of manual inspection time in a software based restoration environment. The throughput requirement for analysis of dust and other defects is reached by sparse application of the detectors in the image sequence while retaining sufficient detection accuracy. Analysis result metadata are represented in a MPEG-7 standard compliant way. The proposed defect summary visualization tools facilitate efficient exploration of visually impaired content by the user.

10.1109/icip.2007.4379941

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Citations (9)

Unsupervised mesh based segmentation of moving objects

Andreas Kriechbaum Roland Mörzinger Georg Thallinger

Multimedia analysis usually deals with a large amount of video data with a significant number of moving objects. Often it is necessary to reduce the amount of data and to represent the video in terms of moving objects and events. Event analysis can be built on the detection of moving objects.

10.1145/1463542.1463544

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