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Manos Papadakis

Researcher at University of Houston

Publications -  71
Citations -  822

Manos Papadakis is an academic researcher from University of Houston. The author has contributed to research in topics: Wavelet & Multiresolution analysis. The author has an hindex of 15, co-authored 69 publications receiving 772 citations. Previous affiliations of Manos Papadakis include University of Ioannina & National and Kapodistrian University of Athens.

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Journal ArticleDOI

Perinasal Imaging of Physiological Stress and Its Affective Potential

TL;DR: A novel framework for quantifying physiological stress at a distance via thermal imaging that associates high stress levels with novice surgeons, while low stress levels are associated with experienced surgeons, raising the possibility for an affective measure (stress) to assist in efficacy determination.
Journal ArticleDOI

Image denoising using a tight frame

TL;DR: A general mathematical theory for lifting frames is presented that allows us to modify existing filters to construct new ones that form Parseval frames, and a new image denoising algorithm is proposed, tailored to the specific properties of these new frame filters.
Journal ArticleDOI

The geometry and the analytic properties of isotropic multiresolution analysis

TL;DR: The main results are the characterization of IMRAs in terms of the Lax–Wiener Theorem, and the characterization and application of IMRA wavelets to 2D and 3D-texture segmentation in natural and biomedical images.
Journal ArticleDOI

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

TL;DR: A novel representation, the Minimum Shape-Cost (MSC) Tree, is introduced that approximates the dendrite centerline with sub-voxel accuracy and is demonstrated to be the uniqueness of such a shape representation as well as its computational efficiency.
Proceedings ArticleDOI

Image denoising using a tight frame

TL;DR: A general mathematical theory for lifting frames is presented that allows us to modify existing filters to construct new ones that form Parseval frames, and a new image denoising algorithm is proposed, tailored to the specific properties of these new frame filters.