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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
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Journal ArticleDOI
TL;DR: In this paper, an analog implementation of an order-statistics filter based on current-mode techniques is presented, where the circuit is designed using switched-current delay lines and current maximum extractors.
Abstract: An analog implementation of an order-statistics filter based on current-mode techniques is presented in this paper. The circuit is designed using switched-current delay lines and current maximum extractors. These filters could be easily incorporated to smart sensors as well as to smart cameras. SPICE simulation results demonstrate the feasibility of simple analog filters using current-mode techniques.

7 citations

Proceedings ArticleDOI
21 Apr 1997
TL;DR: This work employs a matching-based mosaicing method for reconstructing the scene from the curved surface and derives the necessary number of views in order to represent the entire scene depicted on a cylindrical surface.
Abstract: A set of monocular images of a curved painting is taken from different viewpoints around its curved surface. After deriving the surface localization in the camera coordinate system we backproject the image on the curved surface and we flatten it. We analyze the perspective distortions of the scene in the case when it is mapped on a cylindrical surface. Based on the result of this analysis we derive the necessary number of views in order to represent the entire scene depicted on a cylindrical surface. We employ a matching-based mosaicing method for reconstructing the scene from the curved surface. The proposed method is appropriate to be used for painting reconstruction.

7 citations

Proceedings ArticleDOI
01 Sep 2013
TL;DR: The proposed active classification method provides enhanced classification performance in two publicly available action recognition databases.
Abstract: In this paper, we propose a novel classification method involving two processing steps. Given a test sample, the training data residing to its neighborhood are determined. Classification is performed by a Single-hidden Layer Feedforward Neural network exploiting labeling information of the training data appearing in the test sample neighborhood and using the rest training data as unlabeled. By following this approach, the proposed classification method focuses the classification problem on the training data that are more similar to the test sample under consideration and exploits information concerning to the training set structure. Compared to both static classification exploiting all the available training data and dynamic classification involving data selection for classification, the proposed active classification method provides enhanced classification performance in two publicly available action recognition databases.

7 citations

Proceedings ArticleDOI
01 Oct 1992
TL;DR: A signal-adaptive Maximum Likelihood estimation algorithm is proposed, with local image adaptation based on a moving window, for the processing of Ultrasound (US) B-mode images.
Abstract: New techniques are presented for the processing of Ultrasound (US) B-mode images. A signal-adaptive Maximum Likelihood estimation algorithm is proposed, with local image adaptation based on a moving window. The algorithms are tested on US B-mode images obtained from simulated (phantom) and real liver scans1.

7 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
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

Journal ArticleDOI
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

Journal ArticleDOI
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