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Ioannis Pitas
Researcher at Aristotle University of Thessaloniki
Publications - 826
Citations - 26338
Ioannis Pitas is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Facial recognition system & Digital watermarking. The author has an hindex of 76, co-authored 795 publications receiving 24787 citations. Previous affiliations of Ioannis Pitas include University of Bristol & University of York.
Papers
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Journal ArticleDOI
Audio-Assisted Movie Dialogue Detection
M. Kotti,Dimitrios Ververidis,Georgios Evangelopoulos,I. Panagakis,Constantine Kotropoulos,Petros Maragos,Ioannis Pitas +6 more
TL;DR: An audio-assisted system is investigated that detects if a movie scene is a dialogue or not based on actor indicator functions, which define if an actor speaks at a certain time instant.
Proceedings ArticleDOI
Morphological signal decomposition
TL;DR: A method of signal analysis based on mathematical morphology of gray scale functions that can be used for multiscale signal representation and signal recognition and when separate processing of the simple morphological components is desirable.
Journal ArticleDOI
Video shot-boundary detection using singular-value decomposition and statistical tests
TL;DR: The singular-value decomposition (SVD) method is used to derive a refined low-dimensional feature space from the high-dimensional raw feature space, where similar video patterns are placed together and can be easily clustered.
Journal ArticleDOI
Fast free-vibration modal analysis of 2-D physics-based deformable objects
Stelios Krinidis,Ioannis Pitas +1 more
TL;DR: In this paper, the authors presented an accurate, very fast approach for the deformations of two-dimensional physically based shape models representing open and closed curves, which relies on the determination of explicit deformation governing equations that involve neither eigenvalue decomposition, nor any other computationally intensive numerical operation.
Proceedings ArticleDOI
Object tracking based on morphological elastic graph matching
TL;DR: Comparison with an existing feature-based tracking method using measures based on ground truth data proves the superiority of the proposed method for real-time tracking of objects in video sequences.