scispace - formally typeset
Search or ask a question
Author

Ioannis Pitas

Other affiliations: University of Bristol, University of York, University of Toronto  ...read more
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.


Papers
More filters
Journal ArticleDOI
TL;DR: Experimental results on pedestrian detection indicate the efficiency of the proposed method in shape matching and all online training and both online and offline testing operations can be performed in O(logn) time.

10 citations

Proceedings ArticleDOI
09 Jun 1997
TL;DR: The proposed error concealment scheme exploits reconstructed temporal information from previously decoded frames in order to conceal bitstream errors in all types of frames: I, P, or B, as long as temporal information is available.
Abstract: The problem of errors occurring in MPEG-2 coded video sequences, caused by signal loss during transmission, is examined in this paper and an attempt is made to reconstruct the lost parts at each frame. The proposed error concealment scheme exploits reconstructed temporal information from previously decoded frames in order to conceal bitstream errors in all types of frames: I, P, or B, as long as temporal information is available. Since no such information is available for the first frame (I-frame) of an MPEG-2 coded sequence, another concealment technique is added to the proposed scheme, which uses spatial information from neighbouring macroblocks (MBs). The simulation results compared with other methods prove to be better judging from both PSNR values and the perceived visual quality of the reconstructed sequence. Its quality ameliorates with time.

10 citations

Proceedings ArticleDOI
14 May 2006
TL;DR: A novel temporal video segmentation method that, in addition to abrupt cuts, can detect with very high accuracy gradual transitions such as dissolves, fades and wipes is proposed.
Abstract: A novel temporal video segmentation method that, in addition to abrupt cuts, can detect with very high accuracy gradual transitions such as dissolves, fades and wipes is proposed. The method relies on evaluating mutual information between multiple pairs of frames within a certain temporal frame window. This way we create a graph where the frames are nodes and the measures of similarity correspond to the weights of the edges. By finding and disconnecting the weak connections between nodes we separate the graph to subgraphs ideally corresponding to the shots. Experiments on TRECVID2004 video test set containing different types of shot transitions and significant object and camera motion inside the shots prove that the method is very efficient.

10 citations

Book ChapterDOI
08 Jan 2019
TL;DR: This work proposes an automatic annotation of 3D maps with crowded areas, by projecting 2D annotations that are derived through visual analysis of UAV video frames, and provide semantic heatmaps that are projected on the 3D occupancy grid of Octomap.
Abstract: Enriching the map of the flight environment with semantic knowledge is a common need for several UAV applications. Safety legislations require no-fly zones near crowded areas that can be indicated by semantic annotations on a geometric map. This work proposes an automatic annotation of 3D maps with crowded areas, by projecting 2D annotations that are derived through visual analysis of UAV video frames. To this aim, a fully convolutional neural network is proposed, in order to comply with the computational restrictions of the application, that can effectively distinguish between crowded and non-crowded scenes based on a regularized multiple-loss training method, and provide semantic heatmaps that are projected on the 3D occupancy grid of Octomap. The projection is based on raycasting and leads to polygonal areas that are geo-localized on the map and could be exported in KML format. Initial qualitative evaluation using both synthetic and real world drone scenes, proves the applicability of the method.

10 citations

Book ChapterDOI
11 Apr 2002
TL;DR: A new system for the recognition of visual speech based on support vector machines which proved to be powerful classifiers in other visual tasks is proposed, which offers the advantage of an easy generalization to large vocabulary recognition tasks due to the use of viseme models, as opposed to entire word models.
Abstract: Speech recognition based on visual information is an emerging research field We propose here a new system for the recognition of visual speech based on support vector machines which proved to be powerful classifiers in other visual tasks We use support vector machines to recognize the mouth shape corresponding to different phones produced To model the temporal character of the speech we employ the Viterbi decoding in a network of support vector machines The recognition rate obtained is higher than those reported earlier when the same features were used The proposed solution offers the advantage of an easy generalization to large vocabulary recognition tasks due to the use of viseme models, as opposed to entire word models

10 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
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.

6,384 citations

Journal ArticleDOI
TL;DR: In this article, the authors categorize and evaluate face detection algorithms and discuss relevant issues such as data collection, evaluation metrics and benchmarking, and conclude with several promising directions for future research.
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.

3,894 citations