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.

A novel dimensionality reduction technique based on kernel optimization through graph embedding

Abstract: In this paper, we propose a new method for kernel optimization in kernel-based dimensionality reduction techniques such as kernel principal component analysis and kernel discriminant analysis. The main idea is to use the graph embedding framework for these techniques and, therefore, by formulating a new minimization problem to simultaneously optimize the kernel parameters and the projection vectors of the chosen dimensionality reduction method. Experimental results are conducted in various datasets, varying from real-world publicly available databases for classification benchmarking to facial expressions and face recognition databases. Our proposed method outperforms other competing ones in classification performance. Moreover, our method provides a systematic way to deal with kernel parameters whose calculation was treated rather superficially so far and/or experimentally, in most of the cases.

Anthropocentric Semantic Information Extraction from Movies

Abstract: In this chapter we will describe new methods for anthropocentric semantic video analysis, and will concentrate our efforts to provide a uniform framework by which media analysis can be rendered more useful for retrieval applications as well as for human–computer interaction based application. The main idea behind anthropocentric video analysis is that a film is to be viewed as an artwork and not as a mere of frames following each others. We will show that this kind of analysis which is a straightforward approach of human perception of a movie can finally produce some interesting results of the overall annotation of a video content. “Anthropos” which is the greek word for “human” show the intent of our proposition to concentrate in humans in a movie. Humans are the most essential part of a movie and thus we track down all important features that we can get from low-level and mid-level feature algorithms such as face detection, face tracking, eye detection, visual speech recognition, 3D face reconstruction, face clustering, face verification and facial expressions extraction. All these algorithms produce results which are stored in an MPEG-7 inspired description scheme set which implements the way humans are connecting those features. Therefore as a results we have a structured information of all features that can be found for a specific human (e.g. actor). As it will be shown in this chapter this approach as a straightforward approach of human perception provides a new way of media analysis in the semantic level.

Human-centered 2D/3D video content analysis and description

Abstract: In this paper, we propose a way of using the AudioVisual Description Profile (AVDP) of the MPEG-7 standard for stereo video and multichannel audio content description Our aim is to provide means of using AVDP in such a way, that 3D video and audio content can be correctly and consistently described Since AVDP semantics do not include ways for dealing with 3D audiovisual content, a new semantic framework within AVDP is proposed and examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content are presented

Automatic 3D defects identification in stereoscopic videos

Abstract: 3DTV and 3D cinema have become quite popular during the last few years. It is now well understood that certain 3D video quality issues may have a negative effect in the 3D viewing experience. In this paper, we propose two novel algorithms that exploit available disparity information, in order to detect two disturbing stereoscopic issues, namely Stereoscopic Window Violations (SWV) and bent window effects. The algorithms' performance is tested on a number of examples. The proposed algorithms can be used for assessing the overall quality of stereoscopic video content or in order to enable fixing the detected issues in a post-production stage.

Nonlinear digital signal/image processing: overview and perspectives

Abstract: This paper presents an overview of the various nonlinear digital filter classes that exist in the literature. The properties of these classes are presented as well as their applications. The interrelation of the the filter classes and the efforts towards unification are exposed as well. Finally, the current trends in this area are described.

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.