I
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
More filters
Journal ArticleDOI
A texture-based approach to the segmentation of seismic images
TL;DR: Two new methods are presented for using geometric proximity to reference points in region growing and image segmentation based on Voronoi tessellation and mathematical morphology.
Journal ArticleDOI
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
TL;DR: GAL-DeepLabv3+ as discussed by the authors proposes a graph attention layer to optimize image feature representations for road pothole detection, which can be easily deployed in any existing CNN.
Journal ArticleDOI
Non-linear algorithms for processing biological signals.
Sergio Cerutti,Guy Carrault,Pierre J. M. Cluitmans,A. Kinie,Tarmo Lipping,Nikos Nikolaidis,Ioannis Pitas,Maria G. Signorini +7 more
TL;DR: Different approaches to the analysis of biological signals based on non-linear methods, despite the greater methodological and computational complexity is, in many instances, more successful compared to linear approaches, in enhancing important parameters for both physiological studies and clinical protocols.
Proceedings ArticleDOI
3D facial expression recognition using Zernike moments on depth images
TL;DR: The Zernike moments, which are calculated in the depth image of a 3D facial point cloud, are combined with 3D point clouds and depth images to solve problems arising in facial expression recognition due to affine transformations of the data.
Proceedings Article
Statistical Analysis of Human Facial Expressions
Stelios Krinidis,Ioannis Pitas +1 more
TL;DR: The method has been tested on a variety of sequences with very good results, including a database of video sequences representing human faces changing from the neutral state to the one that represents a fully formed human facial expression.