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.

Copyright protection of 3D images using watermarks of specific spatial structure

Abstract: A novel blind method for 3D image watermarking, robust against geometric distortions, is proposed. A ternary watermark is embedded in a grayscale or a color 3D volume. Construction of watermarks having appropriate structure enables fast and robust watermark detection even after several geometric distortions of the watermarked volume. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks. The proposed method is also robust against lossy compression up to a certain compression ratio.

Automatic Detection of 3D Quality Defects in Stereoscopic Videos Using Binocular Disparity

Abstract: The 3D video quality issues that may disturb the human visual system and negatively impact the 3D viewing experience are well known and become more relevant as the availability of 3D video content increases, primarily through 3D cinema, but also through 3D television. In this paper, we propose four algorithms that exploit available stereo disparity information, in order to detect disturbing stereoscopic effects, namely, stereoscopic window violations, bent window effects, uncomfortable fusion object objects, and depth jump cuts on stereo videos. After detecting such issues, the proposed algorithms characterize them, based on the stress they cause to the viewer’s visual system. Qualitative representative examples, quantitative experimental results on a custom-made video data set, a parameter sensitivity study, and comments on the computational complexity of the algorithms are provided, in order to assess the accuracy and the performance of stereoscopic quality defect detection.

ATG-PVD: Ticketing Parking Violations on A Drone

Abstract: In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.

Video Indexing by Face Occurrence-Based Signatures

Abstract: The extraction of a digital signature from a video segment in order to uniquely identify it, is often a necessary prerequisite for video indexing, copyright protection and other tasks. Semantic video signatures are those that are based on high-level content information rather than on low-level features of the video stream, their major advantage being that they are invariant to nearly all types of distortion. Since a major semantic feature of a video is the appearance of specific people in specific frames, we have developed a method that uses the pre-extracted output of face detection and recognition to perform fast semantic indexing and retrieval of video segments. We give the results of the experimental evaluation of our method on an artificial database created using a probabilistic model of the creation of video.

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.