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 general nonlinear filter structure for image processing

Abstract: A nonlinear filter structure is proposed which can be used for the realization of several digital image filters (e.g., homomorphic order statistics, and morphological) and of other image-processing operations (edge detection and skeletonization). The proposed structure can be implemented in VLSI. Therefore it can be used as a building block in digital image-processing systems. >

An anthropocentric description scheme for movies content classification and indexing

Abstract: MPEG-7 has emerged as the standard for multimedia data content description. As it is in its early age, it tries to evolve towards a direction in which semantic content description can be implemented. In this paper we provide a number of classes to extend the MPEG-7 standard so that it can handle the video media data, in a more uniform way. Many descriptors (Ds) and description schemes (DSs) already provided by the MPEG-7 standard, can help to implement semantics of a media. However, by grouping together several MPEG-7 classes and adding new Ds, better results in movie production and analysis tasks can be produced. Several classes are proposed in this context and we show that the corresponding scheme produces a new profile which is more flexible in all types of applications as they are described in [1].

Fuzzy cell hough transform

Abstract: In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or curves in an image, with better accuracy, especially in noisy images. It is based on a fuzzy split of the Hough Transform parameter space. The parameter space is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split of the parameter space provides the advantage to use the uncertainty of the contour points location, which is increased when noisy images have to be used. Moreover the computation time is slightly increased by this method, in comparison with classical Hough Transform.

Towards automated post-production and semantic annotation of films

Abstract: Film post-production nowadays can be, and most often is, done in the digital domain using computers. There are plenty of tools available for 3D CGI effects, 2D image manipulation of individual frames, color correction and grading, audio-visual synchronization and video editing. Every task, however, still requires a considerable amount of human interaction. In this paper, we will provide an overview of the relevant computer vision technologies which could be used to automate the more time intensive parts of these tasks, provided their performance reaches the high quality standards of the film industry, and semantically annotate the films.

Neural Knowledge Transfer for Sentiment Analysis in Texts with Figurative Language

Abstract: Sentiment analysis in texts, also known as opinion mining, is a significant Natural Language Processing (NLP) task, with many applications in automated social media monitoring, customer feedback processing, e-mail scanning, etc. Despite recent progress due to advances in Deep Neural Networks (DNNs), texts containing figurative language (e.g., sarcasm, irony, metaphors) still pose a challenge to existing methods due to the semantic ambiguities they entail. In this paper, a novel setup of neural knowledge transfer is proposed for DNN-based sentiment analysis of figurative texts. It is employed for distilling knowledge from a pretrained binary recognizer of figurative language into a multiclass sentiment classifier, while the latter is being trained under a multitask setting. Thus, hints about figurativeness implicitly help resolve semantic ambiguities. Evaluation on a relevant public dataset indicates that the proposed method leads to state-of-the-art accuracy.

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.