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.

Feature extraction and statistical analysis of videos for cinemetric applications

Abstract: In this paper, we describe a framework for the extraction of low-level and high level information from movies in order to be used for cinemetric applications. The developed framework analyses the available video content and extracts characteristics related to color, motion, contrast, shot length, tempo, face to frame ratios etc. The extracted information is stored in MPEG 7 AVDP profile format, which is a standard description format that can be imported to related cinemetric applications. We applied the developed framework in a collection of downloaded videos, as well as 3 stereoscopic movies.

Fast parallel DSP algorithms on barrel shifter computers

Abstract: Parallel algorithms on barrel shifter computers for a broad class of 1-D and 2-D signal operators are presented. The max/min selection filter, the moving average filter, and the sorting and sliding window fast Fourier transform algorithms are examined. The proposed algorithms require a significantly smaller number of comparisons/computations than the conventional ones. A novel extension of the barrel shifter networks for 2-D signals is introduced, and the implementation of the proposed algorithms on them is also discussed.

Incremental Training of Multiclass Support Vector Machines

Abstract: We present a new method for the incremental training of multiclass Support Vector Machines that provides computational efficiency for training problems in the case where the training data collection is sequentially enriched and dynamic adaptation of the classifier is required. An auxiliary function that incorporates some desired characteristics in order to provide an upper bound of the objective function which summarizes the multiclass classification task has been designed and the global minimizer for the enriched dataset is found using a warm start algorithm, since faster convergence is expected when starting from the previous global minimum. Experimental evidence on two data collections verified that our method is faster than retraining the classifier from scratch, while the achieved classification accuracy is maintained at the same level.

A new methodology for the measurement of the root canal curvature and its 3D modification after instrumentation

Abstract: Objective: In this study, the three-dimensional (3D) modification of root canal curvature was measured, after the application of Reciproc instrumentation technique, by using cone beam computed tomo...

Using mutual information to indicate facial poses in video sequences

Abstract: Estimation of the facial pose in video sequences is one of the major issues in many vision systems such as face based biometrics, scene understanding for human and others. The proposed method uses a novel pose estimation algorithm based on mutual information to extract any required facial pose from video sequences. The method extracts the poses automatically and classifies them according to view angle. Experimental results on the XM2VTS video database indicated a pose classification rate of 99.2% while it was shown that it outperforms a PCA reconstruction method which was used as a benchmark.

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.