Ioannis Pitas

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.

Dynamic shape learning and forgetting

Abstract: In this paper, we present a system capable of dynamically learning shapes in a way that also allows for the dynamic deletion of shapes already learned. It uses a self-balancing Binary Search Tree (BST) data structure in which we can insert shapes that we can later retrieve and also delete inserted shapes. The information concerning the inserted shapes is distributed on the tree's nodes in such a way that it is retained even after the structure of the tree changes due to insertions, deletions and rebalances these two operations can cause. Experiments show that the structure is robust enough to provide similar retrieval rates after many insertions and deletions.

Computerized Reconstruction of Pulpal Blood Vessels Examined under Confocal Microscope

Abstract: SUMMARY The purpose of this study was the evaluation of 3 different histological methods for studying pulpal blood vessels in combination with 2 types of confocal microscope and computer assisted 3-dimensional reconstruction. 10 human, healthy, free of restorations or caries teeth that were extracted for orthodontic reasons were used. From these teeth, the pulp tissues of 5 were removed, fixed in formalin solution, dehydrated and embedded in paraffin. Serial cross sections 5μm thick were taken from 3 of the above mentioned pulpal tissues and stained with CD34 according to the immunohistochemical ABC technique, while the rest 2 were stained with CD34 and Cy5 by means of immunofluorescence after serial cross sectioning of 10μm. 5 of the 10 teeth were fixed, decalcified, serial cross sectioned (30μm thickness) and stained with eosin. The physical sections were examined under 2 types of confocal laser microscope. Serial images were taken for each section, alignment of the images was followed and finally 3-dimensional reconstructions of the pulpal vessels were achieved. The combined use of immunofluorescence, confocal microscope and automatic segmentation proved to be a useful method for the detailed study of pulpal vasculature. The above method provides deep knowledge of the form and spatial relationship even of the smallest pulpal blood vessels with neighbouring structures like odontoblasts, which are essential for the fully understanding of their role and function within the dental pulp.

Exploiting local and global geometric data relationships in Support Vector Data Description

Abstract: In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments, we simultaneously adopted two graphs that describe local and global geometric training data relationships, respectively. We evaluated the proposed classifier in publicly available datasets, where its performance compared favorably against closely related methods.

Face detection by using independent component decomposition

Abstract: In this paper we explore the independent component decomposition for face detection. The minimization of the Kullback Leibler divergence and the maximization of the entropy are two methods employed to decompose an original image into its independent components. We built nearest neighbor classifiers based on their resulting independent components and compare their ability to detect faces to that of support vector machines.

Motivating class-specific nonlinear projections for single and multiple view face verification

Abstract: In this paper we motivate the use of class-specific nonlinear subspace methods for face verification. The problem of face verification is considered as a two-class problem (genuine versus impostor class). The typical Fisher’s Linear Discriminant Analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions. As for the FLDA for N class problems (N > 2) the transformation is not person specific. In order to remedy these limitations of FLDA, exploit the individuality of human faces and take into consideration the fact that the distribution of facial images, under different viewpoints, illumination variations and facial expression is highly complex and non-linear, novel kernel discriminant algorithms are used. The new method was tested in the face verification problem using single and multiple view datasets and found to outperform other commonly used kernel approaches.

I and i

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 …

Face recognition: A literature survey

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.

Detecting faces in images: a survey

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.