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.

Adaptive Nonlinear Filters

Abstract: The nonlinear filters described in the previous chapters are usually optimized for a specific type of noise and sometimes for a specific type of signal. However, this is not usually the case in many applications of nonlinear filtering, especially in image processing. Images can be modeled as two-dimensional stochastic processes, whose statistics vary in the various image regions. Images are nonstationary processes. Furthermore the noise statistics, e.g., the noise standard deviation and even the noise probability density function, vary from application to application, as was described in chapter 3. Sometimes, the noise characteristics vary in the same application from one image to the next. Such cases are the channel noise in image transmission and the atmospheric noise (e.g., the cloud noise) in satellite images. In these environments non-adaptive filters cannot perform well because their characteristics depend on the noise and signal characteristics, which are unknown. Therefore, adaptive filters are the natural choice in such cases. Their performance depends on the accuracy of the estimation of certain signal and noise statistics, namely the signal mean and standard deviation and the noise standard deviation. The estimation is usually local, i.e., relatively small windows are used to obtain the signal and noise characteristics. An important property of these estimators is their robustness to impulse noise, which is present in many image processing applications. Another reason for using adaptive filters is the fact that edge information is very important for the human eye and must be preserved. Certain filters, e.g., the moving average, perform well in homogeneous image regions but fail close to edges. The opposite is true for other filters, e.g., for the median. A combined filter which performs differently in the image edges than in the image plateaus can be used in such a case. These filters are also called decision directed filters because they employ an edge detector to decide if an edge is present or not. Decision directed filtering can also be used in the cases of mixed additive white noise and impulsive noise. Impulses can be detected and removed before the additive noise filtering is performed. Another approach related to decision directed filtering is the two-component model filtering. An image is assumed to consist of two components, the low-pass and the high-pass component. The first one is mainly related to homogeneous image regions, whereas the second one is related to edge information. These two components can be processed in different ways. The output of the two corresponding filters can be recombined to give the final filtered image. The two-component image processing model has been used both for noise removal and image enhancement applications.

Big Data Analysis for Media Production

Abstract: A typical high-end film production generates several terabytes of data per day, either as footage from multiple cameras or as background information regarding the set (laser scans, spherical captures, etc). This paper presents solutions to improve the integration of the multiple data sources, and understand their quality and content, which are useful both to support creative decisions on-set (or near it) and enhance the postproduction process. The main cinema specific contributions, tested on a multisource production dataset made publicly available for research purposes, are the monitoring and quality assurance of multicamera set-ups, multisource registration and acceleration of 3-D reconstruction, anthropocentric visual analysis techniques for semantic content annotation, and integrated 2-D–3-D web visualization tools. We discuss as well improvements carried out in basic techniques for acceleration, clustering and visualization, which were necessary to deal with the very large multisource data, and can be applied to other big data problems in diverse application fields.

Fast Kernel Matrix Computation for Big Data Clustering

Abstract: Kernel k-Means is a basis for many state of the art global clustering approaches. When the number of samples grows too big, however, it is extremely time-consuming to compute the entire kernel matrix and it is impossible to store it in the memory of a single computer. The algorithm of Approximate Kernel k-Means has been proposed, which works using only a small part of the kernel matrix. The computation of the kernel matrix, even a part of it, remains a significant bottleneck of the process. Some types of kernel, however, can be computed using matrix multiplication. Modern CPU architectures and computational optimization methods allow for very fast matrix multiplication, thus those types of kernel matrices can be computed much faster than others.

A tracking framework for accurate face localization

Abstract: This paper proposes a complete framework for accurate face localization on video frames. Detection and forward tracking are first combined according to predefined rules to get a first set of face candidates. Backward tracking is then applied to provide another set of pos-sible localizations. Finally a dynamic programming algorithm is used to select the candidates that minimize a specific cost function. This method was designed to handle different scale, pose and lighting conditions. The experiments show that it improves the face detection rate compared to a frame-based detector and provides a higher precision than a forward information-based tracker.

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.