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 SOM Variant Based on the Wilcoxon Test for Document Organization and Retrieval

Abstract: A variant of the self-organizing maps algorithm is proposed in this paper for document organization and retrieval. Bigrams are used to encode the available documents and signed ranks are assigned to these bigrams according to their frequencies. A novel metric which is based on the Wilcoxon signed-rank test exploits these ranks in assessing the contextual similarity between documents. This metric replaces the Euclidean distance employed by the self-organizing maps algorithm in identifying the winner neuron. Experiments performed using both algorithms demonstrates a superior performance of the proposed variant against the self-organizing map algorithm regarding the average recallprecision curves.

Enhancing facial expression classification by information fusion

Abstract: The paper presents a system that makes use of the fusion information paradigm to integrate two different sorts of information in order to improve the facial expression classification accuracy over a single feature based classification one. The Discriminant Non-negative Matrix Factorization (DNMF) approach is used to extract a first set of features and an automatically geometrical-based feature extraction algorithm is used for retrieving the second set of features. These features are then concatenated into a single feature vector at feature level. Experiments showed that, when these mixed features are used for classification, the classification accuracy is improved compared with the case when only one type of these features is used.

Exploiting discriminant and SVM constraints in NMF

Abstract: A novel method is introduced for exploiting the support vector machine and additional discriminant constraints in nonnegative matrix factorization. The notion of the proposed method is to find the projection matrix that projects the data to a low-dimensional space so that the data projections have minimum within-class variance, maximum between-class variance and the data projections between the two classes are separated by a hyperplane with maximum margin. Experiments were performed on several two-class UCI data sets, as well as on the Cohn-Kanade database for facial expression recognition. Experimental results showed that the proposed method achieves better classification performance than the state of the art nonnegative matrix factorization and discriminant nonnegative matrix factorization followed by support vector machines classification.

A Robust Roll Angle Estimation Algorithm Based on Gradient Descent.

Abstract: This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the proposed roll angle estimation algorithm takes fewer iterations to achieve the same precision as the previous method.

Corrections to “Pothole Detection Based on Disparity Transformation and Road Surface Modeling”

Abstract: Unfortunately, we made two minor mistakes in the above paper. First of all, the first graph on row (c) in Fig. 11 was same as the third graph on row (c) in Fig. 11 . Secondly, “precision” and “recall” in Table III need to be switched. The correct figure and table have no influence on the discussion and conclusions in the above paper, and they are given here.

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.