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.

Document Organization and Retrieval using Self Organizing Maps and Statistical Language Modeling

Abstract: In this paper we present a method for document organization and retrieval based on statistical language modeling.The proposed method, which is based on the vector model, uses nonlinear interpolation to provide more accurate statistical estimators of the conditional probabilities employed for encoding the context of each word. An information retrieval system is built using the self-organizing map algorithm. In the rst step, the self-organizing architecture is used to cluster the feature vectors and to build clusters of semantically related words. Subsequently, the collection of documents is encoded into vectors and the same algorithm is used to cluster the documents in contextually related classes. The information retrieval system is queried using a sample document and the corresponding precision-recall curve is provided.

A Multiple-UAV Software Architecture for Autonomous Media Production

Abstract: The use of UAVs in media production has taken off during the past few years, with increasingly more functions becoming automated. However, current solutions leave a lot to be desired with regard to autonomy and drone fleet support. This paper presents a novel, complete software architecture suited to an intelligent, multiple-UAV platform for media production/cinematography applications, covering outdoor events (e.g., sports) typically distributed over large expanses. Increased multiple drone decisional autonomy, so as to minimize production crew load, and improved multiple drone robustness/safety mechanisms (e.g., regarding communications, flight regulation compliance, crowd avoidance and emergency landing mechanisms) are supported.

Sorting networks using nonlinear L/sub p/ mean comparators

Abstract: In certain signal processing applications there is a need for fast hardware implementations of sorting algorithms and networks. So far, classical minimum/maximum comparators have been utilized in various sorting network topologies. However, these comparators can not attain high speeds in operation, due to limitations in digital technology. This paper introduces the L/sub p/ comparators, which are based on the theory of nonlinear mean filters. It is shown that the disadvantage of introducing errors is counter-balanced by their faster performance, when compared to the performance of classical comparators. A novel L/sub p/ comparator-based sorting network is also presented, for the fast calculation of the median of a data set. In this implementation, the number of steps required to produce the ordered output is not related to the number of inputs.

Maximum Margin Discriminant Projections for facial expression recognition

Abstract: We present a novel dimensionality reduction method which aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated and separated with maximum margin. The proposed method brings certain advantages, both to data embedding and classification. It improves classification performance, reduces the required training time of the SVM classifier, since it is trained over the projected low dimensional samples and also data outliers and the overall data samples distribution inside classes do not affect its performance. The proposed method has been applied for facial expression recognition in Cohn-Kanade database verifying its superiority in this task, against other state-of-the-art dimensionality reduction techniques.

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.