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Georgios Tziritas

Researcher at University of Crete

Publications -  90
Citations -  5986

Georgios Tziritas is an academic researcher from University of Crete. The author has contributed to research in topics: Image segmentation & Motion estimation. The author has an hindex of 30, co-authored 89 publications receiving 5235 citations. Previous affiliations of Georgios Tziritas include Centre national de la recherche scientifique & École Normale Supérieure.

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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
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Face detection using quantized skin color regions merging and wavelet packet analysis

TL;DR: An efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.
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Dense image registration through MRFs and efficient linear programming.

TL;DR: A novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function is introduced, and efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function.
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Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning

TL;DR: A new exemplar-based framework is presented, which treats image completion, texture synthesis, and image inpainting in a unified manner, and manages to resolve what is currently considered as one major limitation of the BP algorithm: its inefficiency in handling MRFs with very large discrete state spaces.
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MRF Energy Minimization and Beyond via Dual Decomposition

TL;DR: It is shown that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms, which are able to derive algorithms that generalize and extend state-of-the-art message-passing methods, and take full advantage of the special structure that may exist in particular MRFs.