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Zeltser, Anton; Kuijper, Arjan [1. Gutachten]; Kähm, Olga [2. Gutachten]

Entwicklung und Evaluierung eines Systems zur bildbasierten Detektion von Fehlern in Stoffen


Darmstadt, TU, Master Thesis, 2016

Die Algorithmen aus den Bereichen Bildverarbeitung, Computer Vision und Maschinelles Lernen finden in der heutigen Zeit immer häufiger ihre Anwendung bei den Industrieprozessen. In vielen Bereichen der Technik und Industrie sind diese Algorithmen ein wichtiger Bestandteil des Planungs- und Produktionsprozesses geworden. Insbesondere in dem Qualitätskontrollvorgang haben bildbasierte Verfahren eine größere Bedeutung. Diese Verfahren ermöglichen es, die Qualität der Produktion, entsprechend den Qualitätsanforderungen, automatisch und präzise zu überprüfen und mögliche Fehlerteile zu identifizieren. Im Mittelpunkt dieser Masterarbeit steht die Schritt-für-Schritt Entwicklung und Analyse eines Systems (in Form eines Algorithmus) zur bildbasierten Detektion von Fehlern in Materialien. Zur Kontrolle werden gebrauchte Stoffstücke mit folgenden Defekten verwendet: Löcher, Risse und Silikonflecke. Eine Besonderheit bei der Erkennung liegt darin, dass die Materialien im Bild so aussehen, als ob sie unabsichtlich auf einen Tisch geworfen worden sind. Die zu prüfenden Materialien können nicht als eine aufgespannte (2-D) Ebene beschrieben werden. Auf diese Art können vorhandene Textilien im Bild neben Defekten verschiedene Merkmale, wie zum Beispiel Falten oder innere und äußere Ränder, besitzen. Diese zusätzlichen Merkmale könnten irrelevante Informationen für die Suche nach Defekten beinhalten. Um die irrelevante Information zu reduzieren, wird vorgeschlagen, dass ein vorhandenes Bild in Ausschnitte aufgeteilt wird. Damit lässt sich jeder Ausschnitt meistens mit einem Merkmal beschreiben. Diese Bildausschnitte werden als Inputdata für das System genutzt. Ziel der Arbeit ist es, zum einen, mit Hilfe von Algorithmen des Maschinellen Lernens ein System für die Detektion des Defekts in Stoffen aufzubauen,und zum anderen, das System sollte auseinanderhalten, ob ein Stoffstück Verschleiß (Riss oder Loch) oder Silikon besitzt, oder, ob ein Stoffstück fehlerfrei mit Falten ist. Anhand der Ausschnitte wird eine Entscheidung über den Defekt im Bild getroffen. Diese Arbeit zeigt, wie die Algorithmen Local binary patterns in Verbindung mit dem Klassifikationsverfahren Support Vector Machine für die Detektion der Defekte in Stoffen verwendet werden.

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Siegmund, Dirk; Kähm, Olga; Handtke, David

Rapid Classification of Textile Fabrics Arranged in Piles


Proceedings of the 13th International Joint Conference on e-Business and Telecommunications Volume 5

International Joint Conference on e-Business and Telecommunications (ICETE) <13, 2016, Lisbon, Portugal>

Research on the quality assurance of textiles has been a subject of much interest, particularly in relation to defect detection and the classification of woven fibers. Known systems require the fabric to be flat and spread-out on 2D surfaces in order for it to be classified. Unlike other systems, this system is able to classify textiles when they are presented in piles and in assembly-line like environments. Technical approaches have been selected under the aspects of speed and accuracy using 2D camera image data. A patch-based solution was chosen using an entropy-based pre-selection of small image patches. Interest points as well as texture descriptors combined with principle component analysis were part of this evaluation. The results showed that a classification of image patches resulted in less computational cost but reduced accuracy by 3.67%.

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Siegmund, Dirk; Handtke, David; Kähm, Olga

Verifying Isolation in a Mantrap Portal via Thermal Imaging


IWSSIP 2016. Proceedings

International Conference on Systems, Signals and Image Processing (IWSSIP) <23, 2016, Bratislava, Slovakia>

This work presents a system that can be used to ensure that only one individual can pass through a designated transit area (mantrap portal). The developed technical approach uses thermal images to detect humans based on their body heat. A special focus was on the behaviour of the system placed under attack when an intruder tries to overcome the system. The performance was evaluated in empirical testing with a test group, selected according to their physical characteristics. The test scenarios cover changing appearances of individuals and possibly carried objects into the mantrap. Receiver Operating Characteristics (ROC) curves show how the developed system performs. This work concludes with a discussion about a number of challenges and gives an outlook for possible solutions.

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Aginako, Naiara; Irujo Arraiza, Juan; Cuadros, Montse; Raffaelli, Matteo; Kähm, Olga; Damer, Naser; Neto, Joao P. Neto

Multimedia Analysis of Video Sources


SIGMAP 2014. Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications

International Conference on Signal Processing and Multimedia Applications (SIGMAP) <11, 2014, Vienna, Austria>

Law Enforcement Agencies (LEAs) spend increasing efforts and resources on monitoring open sources, searching for suspicious behaviours and crime clues. The task of efficiently and effectively monitoring open sources is strongly linked to the capability of automatically retrieving and analyzing multimedia data. This paper presents a multimodal analytics system, created in cooperation with European LEAs. In particular it is described how the video analytics subsystem produces a workflow of multimedia data analysis processes. After a first analysis of video files, images are extracted in order to perform image comparison, classification and face recognition. In addition, audio content is extracted to perform speaker recognition and multilingual analysis of text transcripts. The integration of multimedia analysis results allows LEAs to extract pertinent knowledge from the gathered information.

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Glaser, Christian; Kuijper, Arjan [Referent]; Kähm, Olga [Referentin]; Damer, Naser [Referent]

Face Liveness Detection Against Image and Video Spoofing Attacks


Darmstadt, TU, Master Thesis, 2013

Many situations require users to log into a computer system. For example to perform private tasks like banking, social interaction or to get access to a secured area. Conventional security driven systems have the disadvantage that passwords or keycards are needed. These passwords or keycards can get lost or stolen resulting in a security risk. To overcome this drawback biometrics use the characteristics of the human body to grand access to a computer system. Beside fingerprint or iris recognition face detection is a popular biometric trait. The reason therefore is that it requires only a usual camera. Most of the current systems have a camera build in anyway. Also face recognition is not very intrusive to the user, which gives a high acceptability of face recognition is biometric trait. In the past though face recognition systems could easily be tricked due to spoofing attempts using pictures or videos of the authenticate user. This thesis analyses current algorithms to counter such spoofing attempts and presents a novel approach. The presented approach will use Machine Learning and Computer Vision to utilize an algorithms by Wu et al. [WRS_12] that can magnify subtle changes in videos to reveal the human pulse. An evaluation of the feasibility of this approach will be given.

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Chingovska, Ivana; Yang, Jinwei; Lei, Zhen; Yi, Dong; Li, Stan Z.; Kähm, Olga; Glaser, Christian; Damer, Naser; Kuijper, Arjan; Nouak, Alexander; Komulainen, Jukka; Pereira, Tiago de Freitas; Gupta, Shubham; Khandelwal, Shubham; Bansal, Shubham; Rai, Ayush; Krishna, Tarun; Goyal, Dushyant; Waris, Muhammad-Adeel; Zhang, Honglei; Ahmad, Iftikhar; Kiranyaz, Serkan; Gabbouj, Moncef; Tronci, Roberto; Pili, Maurizio; Sirena, Nicola; Roli, Fabio; Galbally, Javier; Fierrez, Julian; Pinto, Allan; Pedrini, Helio; Schwartz, William Robson; Rocha, Anderson; Anjos, André; Marcel, Sébastien

The 2nd Competition on Counter Measures to 2D Face Spoofing Attacks


2013 International Conference on Biometrics (ICB)

IAPR International Conference on Biometrics (ICB) <6, 2013, Madrid, Spain>

As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive in form of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.

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Kähm, Olga; Damer, Naser

2D Face Liveness Detection: an Overview



Annual International Conference of the Biometrics Special Interest Group (BIOSIG) <11, 2012, Darmstadt, Germany>

Face recognition based on 2D images is a widely used biometric approach. This is mainly due to the simplicity and high usability of this approach. Nonetheless, those solutions are vulnerable to spoof attacks made by non-real faces. In order to identify malicious attacks on such biometric systems, 2D face liveness detection approaches are developed. In this work, face liveness detection approaches are categorized based on the type of liveness indicator used. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works dealing with face liveness detection works is presented. A discussion is made to link the state of the art solutions with the presented categorization along with the available and possible future datasets. All that aim to provide a clear path for the future development of innovative face liveness detection solutions.

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Iturbe, Mikel; Kähm, Olga [Betreuerin]

Implementation and Comparison of GPU Accelerated (CUDA) Algorithms for Feature Extraction and Matching


Mondragon, Univ., Bachelor Thesis, 2012

This work presents a tattoo identification system based on GPU-accelerated feature extraction and matching algorithms. The system first extracts features from the tattoo image. Then it matches the features found in the input image with a variety of features previously saved in a database or a group of stored images. The most similar image in the database is defined as the image that has the highest number of matches with the input image. The system implements a new feature descriptor vector, based on a previous similar implementation. To enable the remote storage of feature data, a database connection is also added to the system. Final results show that the newly implemented descriptor performs better than the previous existing implementation and it is suitable to use in tattoo identification systems. Thanks to its GPU acceleration, the system is faster than other traditional CPU implementations.