Author
Ioannis Pitas
Other affiliations: University of Bristol, University of York, University of Toronto ...read more
Bio: 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 published on a yearly basis
Papers
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TL;DR: Fourteen human single-rooted mandibular teeth were used for this work and the 3-D reconstruction method has proved to be a useful tool in the study of apical microleakage.
11 citations
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24 Oct 2004TL;DR: A novel method for blind 3D mesh model watermarking applications is proposed that is robust against 3D translation, scaling and mesh simplifications.
Abstract: In this paper, a novel method for blind 3D mesh model watermarking applications is proposed. The method is robust against 3D translation, scaling and mesh simplifications. A pseudo-random watermarking signal is casted in the 3D mesh model by geometrically deforming its vertices, without altering the vertex topology. Prior to embedding and detection, a set of simple transforms is applied to the 3D mesh model. Each sample of the watermark sequence is embedded in a set of vertices rather than in a single vertex in order to deal with mesh simplifications. Experimental results indicate the ability of the proposed method to deal with the aforementioned attacks.
11 citations
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TL;DR: A novel class of nonlinear adaptive filters based on order statistics is presented and an LMS algorithm for their adaptation is proposed, essentially a backpropagation algorithm for the adaptation of coefficients that are used before data sorting.
11 citations
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29 Dec 2011TL;DR: The proposed method combines appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space.
Abstract: Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its discriminant parts has been proposed. However, this approach poses several limitations since it assumes that the underline data distribution forms compact sets which is often unrealistic. To remedy this limitation we regard that data inside each class form various number of clusters and apply a Clustering based Discriminant Analysis. The proposed method combines appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space. Experimental results performed on the Cohn-Kanade database verified the effectiveness of the proposed method in the facial expression recognition task.
11 citations
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01 Jan 2008TL;DR: This paper aims to demonstrate the efforts towards in-situ applicability of artificial intelligence in the area of signal processing and its applications to machine learning.
Abstract: 1 University of Oradea, Department of Computers, Universtatii No1, 410087, Oradea, Romania, alinab@uoradea.ro 2 Tampere University of Technology, Institute of Signal Processing, Korkeakoulunkatu 1, P.O. Box 553, FIN-33101, {andrea.hategan,ioan.tabus}@tut.fi 3 Aristotle University of Thessaloniki, Department of Informatics, Artificial Intelligence and Information Analysis Laboratory, Box 451,Thessaloniki, Greece,
11 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
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TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.
6,384 citations
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3,940 citations
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TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
3,894 citations