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.

Optimized Chamfer Matching for Snake-Based Image Contour Representations

Abstract: In this paper we present a novel method on how to take advantage of the snake representation of target objects, when doing chamfer matching for detection/recognition purposes. In this case several time-consuming steps of classic chamfer matching approaches can be simplified. Moreover, we investigate the possibility of involving fewer pixels from both the target and template object to speed up computations. We introduce an optimization method for such an object reduction, which is valid also in the general application scheme of chamfer matching. Finally, we present our experimental results regarding human body detection

Shape-based interpolation of binary 3-D images using morphological skeletonization

Abstract: In this paper, the morphological skeleton interpolation (MSI) algorithm is presented. It is an efficient shape-based interpolation method used for interpolating slices between successive slices of a 3D object. It is based on the well known mathematical morphology procedure of morphological skeletonization, that is used as a representation of an object. The proposed morphological skeleton matching process provides translation, rotation and scaling information at the same time. The interpolated slices preserve the shape of the original object slices, when the slices have similar shapes. It can also modify the shape of an object when the successive slices have not got similar shapes. Applications on artificial and real data are also presented.

Multimodal Speaker Diarization Utilizing Face Clustering Information

Abstract: Multimodal clustering/diarization tries to answer the question “who spoke when” by using audio and visual information. Diarization consists of two steps, at first segmentation of the audio information and detection of the speech segments and then clustering of the speech segments to group the speakers. This task has been mainly studied on audiovisual data from meetings, news broadcasts or talk shows. In this paper, we use visual information to aid speaker clustering. We tested the proposed method in three full length movies, i.e. a scenario much more difficult than the ones used so far, where there is no certainty that speech segments and video appearances of actors will always overlap. The results proved that the visual information can improve the speaker clustering accuracy and hence the diarization process.

Computerized reconstruction of TEM examined pulpal blood vessels and nerves

Abstract: Five healthy premolars were used for transmission electron microscopy examination and three-dimensional (3D) reconstruction of pulpal blood vessels and their surrounding nerves. Serial Epon thin sections were taken from specimens. After a series of preprocessing steps that included digitization, contrast enhancement, slice alignment, segmentation and interpolation, three dimensional surface representation was performed using the triangulation method. The findings showed that vessels were usually accompanied by a number of myelinated and unmyelinated nerves. The nerves shared a more or less common course with the vessels. Small arteries presented greater number of nerves in their vicinity, compared to small veins. In the sections examined, no direct contact between the nerve trunks and the wall of the vessels was found; the closest distance measured was approximately 67 microns. The common course of vessels and nerves suggested autonomic innervation on part of the myelinated axons.

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.