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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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Proceedings ArticleDOI
10 Jun 2013
TL;DR: A novel view-independent human action recognition method is proposed that effectively addresses the camera viewpoint identification problem, i.e., the identification of the position of each camera with respect to the person's body.
Abstract: In this paper a novel view-independent human action recognition method is proposed. A multi-camera setup is used to capture the human body from different viewing angles. Actions are described by a novel action representation, the so-called multi-view action image (MVAI), which effectively addresses the camera viewpoint identification problem, i.e., the identification of the position of each camera with respect to the person's body. Linear Discriminant Analysis is applied on the MVAIs in order to to map actions to a discriminant feature space where actions are classified by using a simple nearest class centroid classification scheme. Experimental results denote the effectiveness of the proposed action recognition approach.

6 citations

Proceedings ArticleDOI
05 Nov 2008
TL;DR: This method allows for simple Mahalanobis or cosine distance comparison of movements, taking implicitly into account time shifts and internal speed variations, and, thus, aiding the design of a real-time movement recognition algorithm.
Abstract: In this paper a novel method for human movement representation and recognition is proposed. A movement type is regarded as a unique combination of basic movement patterns, the so-called dynemes. The fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space and allow the expression of a posture in terms of these dynemes. In the so-called dyneme space, the sparse posture representations of a movement are combined to represent the movement as a single point in that space, and linear discriminant analysis (LDA) is further employed to increase movement type discrimination and compactness of representation. This method allows for simple Mahalanobis or cosine distance comparison of movements, taking implicitly into account time shifts and internal speed variations, and, thus, aiding the design of a real-time movement recognition algorithm.

6 citations

Proceedings ArticleDOI
06 Jul 2016
TL;DR: Experimental results have shown that increased performance can be obtained by employing the proposed Kernel Subclass Support Vector Data Description classifier, and it is extended to work in feature spaces of arbitrary dimensionality.
Abstract: In this paper, we present the Kernel Subclass Support Vector Data Description classifier. We focus on face recognition and human action recognition applications, where we argue that sub-classes are formed within the training class. We modify the standard SVDD optimization problem, so that it exploits subclass information in its optimization process. We extend the proposed method to work in feature spaces of arbitrary dimensionality. We evaluate the proposed method in publicly available face recognition and human action recognition datasets. Experimental results have shown that increased performance can be obtained by employing the proposed method.

6 citations

Proceedings ArticleDOI
01 Feb 2007
TL;DR: A novel approach for estimating 3D head pose in single-view video sequences by using a feature vector which is a by-product of the equations that govern the deformation of the surface model used in the tracking.
Abstract: This paper presents a novel approach for estimating 3D head pose in single-view video sequences. Following initialization by a face detector, a tracking technique that utilizes a 3D deformable surface model to approximate the image intensity is used to track the face in the video sequence. Head pose estimation is performed by using a feature vector which is a by-product of the equations that govern the deformation of the surface model used in the tracking. The afore-mentioned vector is used for training support vector machines (SVM) in order to estimate the 3D head pose. The proposed method was applied to IDIAP head pose estimation database. The obtained results show that the proposed method can achieve an accuracy of 82% if angles are estimated in 10deg increments and 75% if angle are estimated in 5deg increments.

6 citations

Proceedings ArticleDOI
01 May 1994
TL;DR: In this paper, a minimum mean-squared error (MMSE) L-filter is designed on the basis of a multiplicative noise model by using the histogram of grey values as an estimate of the parent distribution of the noisy observations and asuitable estimate of original signal in the corresponding region.
Abstract: In this paper, we introduce segmentation-based L-filters, that is, filtering processes combining segmentationand (nonadaptive) optimum L-filtering, and we use them for the suppression of speckle noise in ultrasonic (US)images. With the aid of a suitable modification of the Learning Vector Quantizer (LVQ) self-organizing neuralnetwork, the image is segmented in regions of approximately homogeneous first-order statistics. For each suchregion a minimum mean-squared error (MMSE) L-filter is designed on the basis of a multiplicative noise modelby using the histogram of grey values as an estimate of the parent distribution of the noisy observations and asuitable estimate of the original signal in the corresponding region. Thus, we obtain a bank of L-filters that arecorresponding to and are operating on different image regions. Simulation results on a simulated US B-mode imageof a tissue mimicking phantom are presented which verify the superiority of the proposed method as compared toa number of conventional filtering strategies in terms of a suitably defined signal-to-noise ratio (SNR) measure anddetection theoretic performance measures.

6 citations


Cited by
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Journal ArticleDOI

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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