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.

Exploiting disparity information in visual object tracking

Abstract: A novel method is proposed for visual object tracking in stereo videos. The algorithm employs Local Steering Kernel features and 2-dimensional color-disparity histograms for object texture description. The proposed framework requires no information about the intrinsic and extrinsic parameters of the stereo camera system. Therefore, it can be applied on 3D video content captured by commercial stereo cameras, as well as 3D movies and 3D TV programs. Experiments showed that the proposed method is effective in tracking objects under partial occlusion and changes in the object view angle.

New affine projection algorithms based on Gauss-Seidel method

Abstract: This paper proposes a robust and stable fast affine projection algorithm based on the Gauss-Seidel method, the so called modified Gauss-Seidel fast affine projection algorithm. The proposed algorithm is generalized for simplified Volterra filters as well. The computational complexity of both the modified Gauss-Seidel fast affine projection algorithm and its generalization for simplified Volterra filters is derived and their performance for acoustic echo cancellation is assessed.

Adaptive LMS L-filters for smoothing noisy images

Abstract: Several adaptive LMS L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and being compared in this paper. First, the location-invariant LMS L-filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. Subsequently, the normalized and the sign LMS L-filters are studied. It is shown that both these filters turn to be identical for a certain choice of the adaptation step-size. A modified LMS L-filter with nonhomogeneous step-sizes is also proposed in order to accelerate the rate of convergence of the adaptive L-filter. Finally, a signal-dependent adaptive filter structure is developed to allow a separate treatment of the pixels that are close to the edges from the pixels that belong to homogeneous image regions.

Motion and segmentation prediction in image sequences based on moving object tracking

Abstract: The image sequence is represented as a set of moving regions which make up moving objects. Motion, position and gray level (or color) information is used for segmenting the moving objects. A criterion is proposed for modeling the 3-D motion and segmentation. After identifying the occluding regions, the moving objects are tracked over the next frames. Prediction is employed for estimating the future moving object position and its optical flow.

Metadata Description of Thermal Videos for Rescue Operations

Abstract: In this paper we present a metadata scheme for the content description of thermal videos captured during rescue operations. The formal description is provided in terms of an XML Schema, thus the content description files have XML format. The schema was designed to provide highly detailed description possibilities for the movie scene. The scheme description, which is implemented in full compatibility with the MPEG-7 standard, provides fast and simple integration capabilities for database applications, and supports video/image indexing and retrieval for further use. It has been already tested that the proposed schema can be conceptualized in ontology services, which have other input besides video analysis.

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.