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.

Human movement recognition using fuzzy clustering and discriminant analysis

Abstract: In this paper a novel method for human movement representation and recognition is proposed. A movement is regarded as a sequence of basic movement patterns, the so-called dynemes. Initially, the fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space, and then principal component analysis plus linear discriminant analysis (PCA plus LDA) is employed to project the postures of a movement to the identified dynemes. In this space, the posture representations of the movement are combined to represent the movement in terms of its comprising dynemes. This representation allows for efficient Mahalanobis or cosine-based nearest centroid classification of variable length movements.

PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation

Abstract: Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optimizing the v-disparity map using dynamic programming. This linear model is then utilized to reduce the redundant information in the left and right road images. The right image is also transformed into the left perspective view, which greatly enhances the road surface similarity between the two images. Finally, the processed stereo images and their disparity maps are concatenated to create a set of 3D images, which are then utilized to train our neural network. The experimental results illustrate that our network achieves a maximum F1-measure of approximately 91.19%, when analyzing the images from the KITTI road dataset.

A fast implementation of two-dimensional weighted median filters

Abstract: This paper deals with the implementation of a fast algorithm for two-dimensional weighted median filtering. Because of the vast amount of data that must be handled, the development of fast algorithms is very important. A fast running algorithm for weighted median filtering, which is based on using a histogram and updating it, is proposed. Experimental results prove the superiority of the proposed algorithm over that of finding the weighted median either by sorting with the Quick Sort or by selecting the r-th order statistic.

CAML – A Universal Configuration Language for Dialogue Systems

Abstract: In this paper, a novel architecture of a universal dialogue system and its configuration language, so-called Conversational Agent Markup Language (CAML), is proposed. The dialogue system embodies a CLIPS engine in order to enable CAML to formulate procedural and heuristic knowledge. CAML supports frames, functions, and categories that enable it: (a) to process wildcards, to control the inner state through variables, and to formulate procedural knowledge in contrast to Phoenix/CAT Dialog Manager; (b) to support nested macros, to control the inner state through variables, to assign priorities and weights to states, and to interface with external databases in contrast to Dialog Management Tool Language (DMTL); (c) to implement context-free grammars, to extract semantic content from user input through frames, to allow numeric variables, and to interface with external databases as opposed to Artificial Intelligence Markup Language (AIML). The proposed system is extensible in the sense that it can be embedded in any conversational system that receives and emits XML content. Such a dialogue system can be incorporated in multimodal interfaces, such as talking head applications, conversational web interfaces, conversational database interfaces, and conversational programming interfaces.

Object tracking based on local steering kernels for drinking activity recognition

Abstract: A novel method for object tracking in videos for drinking activity recognition is proposed. The query object is detected in the first video frame, extracting a new query image. The obtained query image is then compared with patches within a determined region of interest around the position of the detected object in the previous frame. For each image, the local steering kernels are extracted and the similarity between the query image and the patches of the video frame is measured by calculating the matrix cosine similarity. The proposed method finds application in drinking activity recognition, by tracking the object, i.e., the glass, being used.

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.