Abstract-Band - Fakultät für Informatik, TU Wien - Technische ...

Michael Opitz
Text Detection and Recognition in Natural Scene Images
Studium: Masterstudium Visual Computing
BetreuerIn: Ao.Univ.Prof. Dr. Robert Sablatnig
Text detection and recognition in natural scene images has applications in
computer vision systems such as license plate detection, automatic street sign
translation, image retrieval and help for visually impaired people. Scene text,
however, may have complex background, image blur, partially occluded text,
variations in font-styles, image noise and varying illumination. Hence scene
text recognition is a challenging computer vision problem. This work addresses
the problem of dictionary driven end-to-end scene text recognition, which is
divided into a text detection problem and a text recognition problem. For text
detection an AdaBoost sliding window classifier is used to detect text in
multiple scales. The effectiveness of several feature-sets for this classifier are
compared and evaluated. A modified Local Ternary Pattern (LTP) feature-set is
found as most effective for text detection. In a post-processing stage Maximally
Stable Extremal Regions (MSER) are detected and labeled as text or non-text.
Text regions are grouped to textlines. Textlines are split into words by a wordsplitting
method build upon k-means and linear Support Vector Machines
(SVM). For text recognition a deep Convolutional Neural Network (CNN) trained
with backpropagation is used as one-dimensional sliding window classifier. To
avoid overfitting the network is regularized by Dropout. Recognition responses
are used in a Viterbi-style algorithm to find the most plausible word in a
dictionary. The influence of training set size and size of convolutional layers is
evaluated. The system presented outperforms state of the art methods on the
ICDAR 2003 and 2011 dataset in the text-detection (F-score: 74.2% / 76.7%),
dictionary-driven cropped-word recognition (F-score: 87.1% / 87.1%) and
dictionary-driven end-to-end recognition (F-score: 72.6% / 72.3%) tasks.
Thomas Pönitz
Efficient Retrieval of Near-Duplicate Images
Studium: Masterstudium Visual Computing
BetreuerIn: Privatdoz. Dr. Martin Kampel
Aufgrund der kontinuierlich steigenden Menge an digitalen Bildern und dem
ständigen Wachstum an verfügbarer Speicherkapazität, sind umfangreiche
Bilddatenbanken weit verbreitet. Eine spezielle Herausforderung ist eine Bilddatenbank
auf nahezu identische Bilder zu durchsuchen. Ein dafür konzipiertes
System akzeptiert Bilder als Suchanfragen und liefert gegebenenfalls entsprechende
Originale zurück. Die Schwierigkeit dabei ist eine Bildrepräsentation
zu finden die robust in Bezug auf diese Transformationen ist. Gleichzeitig
soll die Ähnlichkeit zweier Repräsentationen effizient zu berechnen sein
oder die Repräsentationen sollten indizierbar sein. Im ersten Fall kann die
Datenbank linear durchsucht werden, während im zweiten Fall eine effiziente
Suchstruktur aufgebaut werden kann. Die Bags of Visual Words Methode hat
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