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.

The use of 3D computerized reconstruction for the study of coronal microleakage

Abstract: Aim The purpose of this study was to evaluate a new method for studying coronal microleakage associated with root-filled teeth. Methodology Twenty human mandibular posterior teeth were prepared chemomechanically with the stepback technique and then divided into two groups of 10 teeth each. The canals were filled using lateral condensation of gutta-percha points; Roth’s 801 was used as a sealer for the first group, and Ketac-Endo for the second. After 48 h, the temporary fillings in the access cavities were removed, the roots coated with three layers of nail polish and then the teeth were exposed to artificial saliva for 40 days. Subsequently, the crowns of the teeth were placed into Indian ink for 4 days before the coating was removed and the teeth embedded in a two-phase polyester resin. Serial cross sections were taken from each specimen using a microtome, and each cross section photographed under a stereoscopic microscope. The photographs of the cross sections were digitized using an image scanner and the contours of the external surface of the teeth, the obturated root canals and the boundaries of dye penetration were followed. Finally, a three-dimensional surface representation was achieved using the triangulation method. Results Although the sample size and the experimental methods were not designed to form valid groups, the results showed that all roots obturated with Ketac-Endo sealer had dye microleakage, whilst only three teeth of the Roth’s 801 group had dye microleakage. Conclusions The 3D reconstruction of dye microleakage proved to be an interesting method and a useful tool for the evaluation of coronal microleakage when using different types of root canal sealers.

Multichannel transforms for signal/image processing

Abstract: This paper presents a novel approach to the Fourier analysis of multichannel time series. Orthogonal matrix functions are introduced and are used in the definition of multichannel Fourier series of continuous-time periodic multichannel functions. Orthogonal transforms are proposed for discrete-time multichannel signals as well. It is proven that the orthogonal matrix functions are related to unitary transforms (e.g., discrete Hartley transform (DHT), Walsh-Hadamard transform), which are used for single-channel signal transformations. The discrete-time one-dimensional multichannel transforms proposed in this paper are related to two-dimensional single-channel transforms, notably to the discrete Fourier transform (DFT) and to the DHT. Therefore, fast algorithms for their computation can be easily constructed. Simulations on the use of discrete multichannel transforms on color image compression have also been performed.

Floating point error analysis of two-dimensional, fast Fourier transform algorithms

Abstract: Floating-point error is conducted for three algorithms commonly used for the calculation of two-dimensional fast Fourier transforms (FFTs), namely, the conventional row-column FFT, the vector-radix FFT, and the polynomial-transform FFT The respective errors are determined both analytically and on the basis of computer simulation Comparison shows that the vector-radix FFT and the polynomial-transform FFT, even though computationally more efficient than the row-column FFT, show approximately the same (and sometimes reduced) susceptibility to errors in floating-point arithmetic >

Convolutional Neural Networks for Visual Information Analysis with Limited Computing Resources

Abstract: Over the past decade, Deep Convolutional Neural Networks with heavy architectures and large numbers of parameters have achieved state-of-the-art results and eclipsed other methods in multiple visual analysis tasks, including object detection. However, the real-time requirements of such tasks directly conflict with the restricted computational capabilities of embedded systems, prohibiting the immediate deployment of bulky models, and necessitating their optimization for inference. Parameter pruning techniques reduce the number of parameters while reducing the input size leads to smaller internal representations, leading by extension to fewer computational operations. Furthermore, inference optimization schemes provided by Deep Learning frameworks can yield significant speed ups, for example by allowing half-precision floating point operations. We investigate the behavior of various model configurations in object detection tasks and perform a comparative study on inference optimization methods which aim to reduce the computational cost of Convolutional Neural Networks, while examining the effect of such methods on their performance, and propose architecture modifications for this purpose.

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.