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.

FISH Image Analysis Using a Modified Radial Basis Function Network

Abstract: Fluorescent in situ hybridization (FISH) is a valuable method for determining Her-2/neu status in breast carcinoma samples, an important prognostic indicator. Visual evaluation of FISH images is a difficult task which involves manual counting of dots in multiple images, a procedure which is both time consuming and prone to human error. A number of algorithms have recently been developed dealing with (semi)-automated analysis of FISH images. These algorithms are quite promising but further improvement is required in improving their accuracy. Here, we present a novel method for analyzing FISH images based on the statistical properties of Radial Basis Functions. Our method was evaluated on a data set of 100 breast carcinoma cases provided by the Aristotle University of Thessaloniki and the University of Pisa, with promising results.

Robust and adaptive techniques in self-organizing neural networks

Abstract: Robust and adaptive training algorithms aiming at enhancing the capabilities of self-organizing and Radial Basis Function (RBF) neural networks are reviewed in this paper. The following robust variants of Learning Vector Quantizer (LVQ) are described: the order statistics LVQ, the L 2 LVQ and the split-merge LVQ. Successful application of the marginal median LVQ that belongs to the class of order statistics LVQs in the self-organized selection of the centers in RBF neural networks is reported. Moreover, the use of the median absolute deviation in the estimation of the covariance matrix of the observations assigned to each hidden unit in RBF neural networks is proposed. Applications that prove the superiority of the proposed variants of LVQ and RBF neural networks in noisy color image segmentation, color-based image recognition, segmentation of ultrasonic images, motion-field smoothing and moving object segmentation are outlined.

Dynamic action classification based on iterative data selection and Feedforward Neural networks

Abstract: In this paper we present a dynamic classification scheme involving Single-hidden Layer Feedforward Neural (SLFN) network-based non-linear data mapping and test sample-specific labeled data selection in multiple levels The number of levels is dynamically determined by the test sample under consideration, while the use of Extreme Learning Machine (ELM) algorithm for SLFN network training leads to fast operation The proposed dynamic classification scheme has been applied to human action recognition by employing the Bag of Visual Words (BoVW)-based action video representation providing enhanced classification performance compared to the static classification approach

Still Image and Video Fingerprinting

Abstract: Multimedia fingerprinting, also know as robust/perceptual hashing and replica detection is an emerging technology that can be used as an alternative to watermarking for the efficient Digital Rights Management (DRM) of multimedia data. Two fingerprinting approaches are reviewed in this paper. The first is an image fingerprinting technique that makes use of color and texture descriptors,R-trees and Linear Discriminant Analysis (LDA). The second is a two-step, coarse-to-fine video fingerprinting method that involves color-based descriptors, R-trees and a frame-based voting procedure. Experimental performance evaluation is provided for both methods.

Select and split fuzzy cell Hough transform-a fast and efficient method to detect contours in images

Abstract: In this paper a new variation of Hough transform is proposed. The parameter space of Hough Transform is iteratively split into fuzzy cells which are defined as fuzzy numbers. Each fuzzy cell corresponds to a fuzzy curve in the spatial domain. After each iteration the fuzziness of the cells is reduced and the curves are estimated with better accuracy. The uncertainty of the contour point location is transferred to the parameter space and gives better accuracy in curve estimation than the classical Hough transform, especially when noisy images have to be used. Moreover, the computation time is significantly decreased, since the regions of the parameter space where contours do not correspond, are rejected during the iterations.

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.