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.

Cellular LMS L-filters for noise suppression in still images and image sequences

Abstract: A novel class of nonlinear adaptive L-filters based on cellular neural networks topology is presented. Like cellular neural systems and cellular automata as well, processing nodes, called cells, communicate with each other directly only through its nearest neighbors exchanging information. Each cell is an adaptive LMS L-filter. The proposed filters share the best features of both adaptive filters and cellular neural network topologies; their adaptive structure tracks image nonstationarities and their local interconnection feature makes it suitable for VLSI implementation. Cellular adaptive LMS L-filters are suited for high-speed parallel adaptive image filtering. Some interesting applications to image and image sequence filtering are demonstrated. >

Fast CNN-Based Single-Person 2D Human Pose Estimation for Autonomous Systems

Abstract: This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human pose estimation from RGB images that balances between high 2D human pose/skeleton estimation accuracy and rapid inference. Thus, it is suitable for safety-critical embedded AI scenarios in autonomous systems, where computational resources are typically limited and fast execution is often required, but accuracy cannot be sacrificed. The architecture is composed of a shared feature extraction backbone and two parallel heads attached on top of it: one for 2D human body joint regression and one for global human body structure modelling through Image-to-Image Translation (I2I). A corresponding multitask loss function allows training of the unified network for both tasks, through combining a typical 2D body joint regression with a novel I2I term. Along with enhanced information flow between the parallel neural heads via skip synapses, this strategy is able to extract both ample semantic and rich spatial information, while using a less complex CNN; thus it permits fast execution. The proposed architecture is evaluated on public 2D human pose estimation datasets, achieving the best accuracy-speed ratio compared to the state-of-the-art. Additionally, it is evaluated on a pedestrian intention recognition task for self-driving cars, leading to increased accuracy and speed in comparison to competing approaches.

Anthropocentric video analysis for film and games postproduction

Abstract: The interest of the scientific community for anthropocentric (human-centered) video analysis stems from the fact that the extracted information (e.g. human presence, identity, body posture, emotional status, body parts movements, activities) can be utilised in various important applications. One such application domain is film and games postproduction, where the anthropocentric video analysis results can be used in various tasks, such as audiovisual material indexing and retrieval or automatic semantic annotation. In this paper, we shall review recent research results in a number of diverse areas, such as face/person detection, human activity recognition and face/facial expression recognition, either from a single or multiview visual (image/video) sources.

Fast constrained person identity label propagation in stereo videos using a pruned similarity matrix

Abstract: In this paper, a novel video data (more specifically facial images) fast labeling method, that aims in the acceleration of a state of the art facial identity label propagation technique is presented. Our method assumes that facial images are derived by applying facial image tracking on stereoscopic videos and thus are temporally ordered. The proposed method utilizes a pruned similarity matrix so that the facial label inference is conducted using fewer entries in this matrix, namely the pairwise similarities of the facial images that exist in the main and the N upper and lower off-diagonals. The proposed method can also incorporate pairwise facial image similarity and dissimilarity constraints into the objective function of the label propagation. Experiments which have been conducted on facial image labeling in three stereoscopic movies, confirm the increased labeling accuracy and the reduced computational cost of the proposed method.

Compression Optimized Tracing of Digital Curves using Graph Theory

Abstract: The use of an alphabet of line segments to compose a curve is a possible approach for curve data compression. An existing state-of-the-art method considers a quadtree decomposition of the curve to perform the substitution of the curve parts from the alphabet of line segments. In this paper, we propose a graph theory based algorithm for tracing the curve directly to eliminate the quadtree decomposition needs. This approach obviously improves the compression efficiency, as longer line segments can be used. We tune our method further by selecting optimal turns at junctions during tracing the curve. We also discuss briefly how other application fields can take advantage of the presented approach.

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.