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.

Greedy Salient Dictionary Learning for Activity Video Summarization

Abstract: Automated video summarization is well-suited to the task of analysing human activity videos (e.g., from surveillance feeds), mainly as a pre-processing step, due to the large volume of such data and the small percentage of actually important video frames. Although key-frame extraction remains the most popular way to summarize such footage, its successful application for activity videos is obstructed by the lack of editing cuts and the heavy inter-frame visual redundancy. Salient dictionary learning, recently proposed for activity video key-frame extraction, models the problem as the identification of a small number of video frames that, simultaneously, can best reconstruct the entire video stream and are salient compared to the rest. In previous work, the reconstruction term was modelled as a Column Subset Selection Problem (CSSP) and a numerical, SVD-based algorithm was adapted for solving it, while video frame saliency, in the fastest algorithm proposed up to now, was also estimated using SVD. In this paper, the numerical CSSP method is replaced by a greedy, iterative one, properly adapted for salient dictionary learning, while the SVD-based saliency term is retained. As proven by the extensive empirical evaluation, the resulting approach significantly outperforms all competing key-frame extraction methods with regard to speed, without sacrificing summarization accuracy. Additionally, computational complexity analysis of all salient dictionary learning and related methods is presented.

Class-specific discriminant non-negative matrix factorization for frontal face verification

Abstract: In this paper, a supervised feature extraction method having both non-negative bases and weights is proposed. The idea is to extend the Non-negative Matrix Factorization (NMF) algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The proposed method incorporates discriminant constraints inside the NMF decomposition in a class specific manner. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well known XM2VTS database. The proposed algorithm greatly enhance the performance of NMF for frontal face verification.

3D head pose estimation in monocular video sequences by sequential camera self-calibration

Abstract: This paper presents a novel approach for estimating 3D head pose in single-view video sequences acquired by an uncalibrated camera. Following the initialization by a face detector, a tracking technique localizes the faces in each frame in the video sequence. Head pose estimation is performed by using a structure from motion and self-calibration technique in a sequential way. The proposed method was applied to the IDIAP database that contains head pose ground truth data. The obtained results demonstrate that the method can estimate the head pose with satisfying accuracy.

Joint Lightweight Object Tracking and Detection for Unmanned Vehicles

Abstract: In this paper, we address the problem of lightweight and effective visual object tracking and we present a real-time tracking system suitable for integration in embedded autonomous platforms. We propose a novel tracking framework for classification-based re-detection and tracking, with learnable management of tracking and detection results. The proposed framework includes a novel, very efficient object reidentification method, which filters the detection candidates and systematically corrects the tracking results. In our experiments, we demonstrate the effectiveness of the proposed system by comparing its performance against several other state-of-the art trackers and report the results on the UAV123 and UAV20L datasets. The results indicate that the proposed method is significantly more robust and accurate against recent state-of-the-art trackers, surpassing problems caused by real-world scenarios, while maintaining fast tracking speeds, making it suitable for use in real-time vision applications for autonomous robots, such as Unmanned Aerial Vehicles (UAVs).

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.