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====== Image and Video Analysis ======

With the availability of digital imaging devices, users world-wide generate and collect vast amounts of
image and video data, and digital visual content has become an integral part of our everyday life.
Our research targets at the development of novel approaches for exploiting this
content efficiently. This includes intelligent search and browsing
functionality, as well as techniques for extracting higher-level semantic
information directly from the visual content. Our main research topics are:

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===== Image- and Video Retrieval & Browsing =====

To utilise huge image and video collections, advanced search and
browsing functionality is required. We are working on content-based techniques
that do not require a manual acquisition of meta-data, with core aspects
of our research being:
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  * Similarity-based Search
  * Detecting Near-duplicate Images and Video Scenes
  * Robust Sub-image Retrieval
  * Visual Clustering
  * Scalability of Image and Video Search

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===== Visual Classification & Concept Detection =====

The automatic recognition of semantic concepts in image and video data is a core challenge of modern multimedia management, opening applications like intelligent content filters, surveillance, personal video recommendation, or content-based advertisement. We focus on the following research topics in the area:
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  * Auto-Annotation
  * Visual Learning from Web Sources
  * Scene Recognition
  * Pornography Detection

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===== Text in Image Recognition =====

While optical character recognition (OCR) is well-developed in
the document analysis domain, the detection and recognition of textual
information in arbitrary image data is still an unsolved research topic.
Here, recognition algorithms have to deal with challenges like
strongly varying illumination and scale, different forms of distortion, and uncommon
fonts. Our work addresses the following topics:
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  * Text Detection
  * Recognition of Overlay Text
  * Recognition of Scene Text

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====== Document Image Analysis ======

In spite of the increasing relevance of electronic documents, the
digitalisation and processing of traditional paper-based documents is still a
crucial step in many business processes. MADM is developing advanced algorithms
for supporting the automated analysis and interpretation of document images
with a focus on the following topics:

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===== Layout Analysis =====

Beyond the digitalisation of pure textual information with Optical Character
Recognition (OCR) algorithms, the interpretation of the layout is also crucial for understanding and digitising the content of a document. We are working on basic layout analysis technologies including
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  * Intelligent Document Image Preprocessing Algorithms
  * Geometric Layout Analysis and Page Segmentation 
  * Logical Layout Analysis and Reading Order Detection

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===== Document Counterfeit Detection =====

Another focus of our research are novel approaches for verifying the
originality of sensitive documents like receipts, certificates, or contracts. Important research topics here are:
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  * Copy Detection
  * Printing Technique Classification
  * Printer Identification
  * Geometric Layout Consistency Verification
  * Digital Document Fingerprints

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====== Data Mining ======

Beyond the work in the image and video domain, MADM is also conducting research in other data mining domains. 

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===== Pattern Recognition Engineering =====

One goal of this research is to make pattern recognition algorithms more accessible to real-world software developers which don't have a deep expertise in these algorithms. Research topics here are:
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  * Automated System Evaluation
  * Automated Model Construction
  * Meta Learning and Classifier Optimization
  * Collaborative Development Tools


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===== Anomaly Detection =====

An important issue in many application domains is the detection of anomalous data instances and data patterns. A popular example is the detection of misuse in financial transaction data or telecommunication systems. 

We are developing statistical anomaly detection algorithms with a focus on unsupervised scenarios where neither labeled normal nor labeled anomalous data samples are available for training (semi-)supervised algorithms. Central research topics here are:
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  * Nearest Neighbor based Approaches
  * Clustering-based Approaches
  * Scalability
  * Efficient Application on Real World Data Sets 

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===== Network Security =====

Another goal is the application of statistical pattern recognition algorithms in [[netsec|network security applications]]:
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  * Detection and Mitigation of Distributed Denial of Service (DDoS) Attacks
  * Anomaly Detection in Computer Networks

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===== Financial & Business Applications =====

Moreover, we have expertise in applying pattern recognition and machine learning approaches in financial and business applications. Typical application examples here are credit scoring and fraud detection.


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