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.

3D Object Pose Estimation Using Multi-Objective Quaternion Learning

Abstract: In this paper, a framework is proposed for object recognition and pose estimation from color images using convolutional neural networks (CNNs). 3D object pose estimation along with object recognition has numerous applications, such as robot positioning versus a target object and robotic object grasping. Previous methods addressing this problem relied on both color and depth (RGB-D) images to learn low-dimensional viewpoint descriptors for object pose retrieval. In the proposed method, a novel quaternion-based multi-objective loss function is used, which combines manifold learning and regression to learn 3D pose descriptors and direct 3D object pose estimation, using only color (RGB) images. The 3D object pose can then be obtained either by using the learned descriptors in the nearest neighbor (NN) search or by direct neural network regression. An extensive experimental evaluation has proven that such descriptors provide greater pose estimation accuracy than the state-of-the-art methods. In addition, the learned 3D pose descriptors are almost object-independent and, thus, generalizable to unseen objects. Finally, when the object identity is not of interest, the 3D object pose can be regressed directly from the network, by overriding the NN search, thus significantly reducing the object pose inference time.

Multidimensional cyclic convolution algorithms with minimal multiplicative complexity

Abstract: This paper presents novel multidimensional cyclic convolution algorithms which achieve the theoretically minimum number of multiplications. The proposed algorithms are compared to the well-known and used polynomial transform and split nesting convolution algorithms.

Exploiting discriminant information in elastic graph matching

Abstract: In this paper, we investigate the use of discriminant techniques in the elastic graph matching (EGM) algorithm. First we use discriminant analysis in the feature vectors of the nodes in order to find the most discriminant features. The similarity measure for discriminant feature vectors and the node deformation are combined in a discriminant manner in order to form a local similarity measure between nodes. Moreover, the local similarity values at the nodes of the elastic graph, are weighted by coefficients that are also derived by some discriminant analysis in order to form a total similarity measure between faces. We illustrate the improvements in performance in frontal face verification using a modified multiscale morphological analysis.

Bernoulli shift generated chaotic watermarks: theoretic investigation

Abstract: The paper statistically analyzes the behaviour of chaotic watermark signals generated by n-way Bernoulli shift maps. For this purpose, a simple blind copyright protection watermarking system is considered. The analysis involves theoretical evaluation of the system detection reliability, when a correlator detector is used. The aim of the paper is twofold: (i) to introduce the n-way Bernoulli shift generated chaotic watermarks and theoretically contemplate their properties with respect to detection reliability and (ii) to establish theoretically their potential superiority against the widely used pseudorandom watermarks. Experimental verification of the theoretical analysis results is also performed.

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.