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.

Morphological segmentation of histology cell images

Abstract: Two algorithms for segmentation of cell images are proposed. They have a unique part that contains computation of morphological gradient to extract object borders and thinning the obtained borders to get a line of one-pixel thickness. For this task, we propose the fast gray-scale thinning algorithm that is based on the idea of the analysis of binary image layers. Then, the obtained one-pixel lines are used to extract cells and compute their characteristics. The algorithms based on morphological and split/merge segmentation are developed and used for this task.

High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events

Abstract: Camera-equipped unmanned aerial vehicles (UAVs), or “drones,” are a recent addition to standard audiovisual shooting technologies. As drone cinematography is expected to further revolutionize media production, this paper presents an overview of the state-of-the-art in this area, along with a brief review of current commercial UAV technologies and legal restrictions on their deployment. A novel taxonomy of UAV cinematography visual building blocks, in the context of filming outdoor events where targets (e.g., athletes) must be actively followed, is additionally proposed. Such a taxonomy is necessary for progress in intelligent/autonomous UAV shooting, which has the potential of addressing current technology challenges. Subsequently, the concepts and advantages inherent in multiple-UAV cinematography are introduced. The core of multiple-UAV cinematography consists in identifying different combinations of multiple single-UAV camera motion types, assembled in meaningful sequences. Finally, based on the defined UAV/camera motion types, tools for managing a partially autonomous, multiple-UAV fleet from the director’s point of view are presented. Although the overall focus is on cinematic coverage of sports events, the majority of our contributions also apply in different scenarios, such as movies/TV production, newsgathering, or advertising.

Minimum Class Variance Support Vector Machines

Abstract: In this paper, a modified class of support vector machines (SVMs) inspired from the optimization of Fisher's discriminant ratio is presented, the so-called minimum class variance SVMs (MCVSVMs). The MCVSVMs optimization problem is solved in cases in which the training set contains less samples that the dimensionality of the training vectors using dimensionality reduction through principal component analysis (PCA). Afterward, the MCVSVMs are extended in order to find nonlinear decision surfaces by solving the optimization problem in arbitrary Hilbert spaces defined by Mercer's kernels. In that case, it is shown that, under kernel PCA, the nonlinear optimization problem is transformed into an equivalent linear MCVSVMs problem. The effectiveness of the proposed approach is demonstrated by comparing it with the standard SVMs and other classifiers, like kernel Fisher discriminant analysis in facial image characterization problems like gender determination, eyeglass, and neutral facial expression detection.

A method for watermark casting on digital image

Abstract: Watermark casting on digital images is an important problem since it affects many aspects of the information market. We propose a method for casting digital watermarks on images, and we analyze its effectiveness. The satisfaction of some basic demands in this area is examined, and a method for producing digital watermarks is proposed. Moreover, issues like immunity to subsampling and image-dependent watermarks are examined, and simulation results are provided for the verification of the above-mentioned topics.

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.