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.

Semi-supervised Classification of Human Actions Based on Neural Networks

Abstract: In this paper, we propose a novel algorithm for Single-hidden Layer Feed forward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extend the Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training data representation in the ELM space to this end. The proposed algorithm is evaluated on human action recognition, where its performance is compared with that of other (semi-)supervised classification schemes. Experimental results on two publicly available action recognition databases denote its effectiveness.

Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes

Abstract: Eating and drinking activity recognition can be considered a solitary research field in activity recognition area. The development of an application capable to identify human eating and drinking activity can be really useful in a smart home environment targeting to extend independent living of older persons in the early stages of dementia. In this paper a novel method aiming at eating and drinking activity recognition is presented. Activities are considered as a sequence of human body poses forming 3D volumes, in which the third dimension refers to time. Fuzzy Vector Quantization is performed to associate the 3D volume representation of an activity video with 3D volume prototypes and Linear Discriminant Analysis is used to map activity representations in a low dimensional discriminant feature space. In this space a simple Nearest Centroid classification procedure leads to very satisfactory classification results.

Conservation of metal-coordinating residues.

Abstract: As a result of rapid advances in genome sequencing, the pace of discovery of new protein sequences has surpassed that of structure and function determination by orders of magnitude. This is also true for metal-binding proteins, that is, proteins that bind one or more metal atoms necessary for their biological function. While metal binding site geometry and composition have been extensively studied, no large scale investigation of metal-coordinating residue conservation has been pursued so far. In pursuing this analysis, we were able to corroborate anecdotal evidence that certain residues are preferred to others for binding to certain metals. The conservation of most metal-coordinating residues is correlated with residue preference in a statistically significant manner. Additionally, we also established a statistically significant difference in conservation between metal-coordinating and noncoordinating residues. These results could be useful for providing better insight to functional importance of metal-coordinating residues, possibly aiding metal binding site prediction and design, metal-protein complex structure prediction, drug discovery, as well as model fitting to electron-density maps produced by X-ray crystallography.

Image Watermarking: Techniques and Applications

Abstract: Publisher Summary Watermarking is the practice of imperceptibly altering a piece of data in order to embed information about the data. According to the definition there are two important characteristics of watermarking. First, information embedding should not cause perceptible changes to the host medium. Second, the message should be related to the host medium. In this sense, the watermarking techniques form a subset of information hiding techniques, which also include cases where the hidden information is not related to the host medium. This chapter reviews the main application domains of watermarking. Properties and classification schemes of watermarking techniques are presented followed by the basic functional modules of a watermarking scheme. Further, the principles and techniques devised for two major application areas, namely copyright protection and authentication are discussed.

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.