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 …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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.

Face recognition: A literature survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

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.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

A novel method based on shape and texture information is proposed in this paper for facial expression recognition from video sequences. The Discriminant Non-negative Matrix Factorization (DNMF) algorithm is applied at the image cor-responding to the greatest intensity of the facial expression (last frame of the video sequence), extracting that way the texture information. A Support Vector Machines (SVMs) system is used for the classification of the shape information derived from tracking the Candide grid over the video sequence. The shape information consists of the differences of the node coordinates between the first (neutral) and last (fully expressed facial expression) video frame. Subsequently, fusion of texture and shape information obtained is performed using Radial Basis Function (RBF) Neural Net-works (NNs). The accuracy achieved is equal to 98,2% when recognizing the six basic facial expressions.

/pdf/facial-expression-recognition-using-shape-and-texture-3obzrnbwp7.pdf

Facial expression recognition using shape and texture information

Parallel algorithms and architectures for 2-D butterfly-like transforms

This paper presents a new method for reconstructing paintings from component images. A set of monocular images of a painting from a straight homogeneous generalized cylinder is taken from various viewpoints. After deriving the surface localization in the camera, coordinate system, the images are backprojected on the curved surface and flattened. We derive the perspective distortion of the scene in the case when it is mapped on a cylindrical surface. Based on the result of this study we derive the necessary number of views in order to represent the entire scene depicted on a cylindrical surface. We propose a matching-based mosaicing algorithm for reconstructing the scene from the curved surface. The proposed algorithm is applyed on paintings.

/pdf/mosaicing-of-flattened-images-from-straight-homogeneous-3aqazjqf8y.pdf

Mosaicing of Flattened Images from Straight Homogeneous Generalized Cylinders

This paper provides an overview of recent research efforts for digital media analysis and description. It focuses on the specific problem of human centered video analysis for activity and identity recognition in unconstrained environments. For this problem, some of the state-of-the-art approaches for video representation and classification are described. The presented approaches are generic and can be easily adapted for the description and analysis of other semantic concepts, especially those that involve human presence in digital media content.

Computational Intelligence Approaches for Digital Media Analysis and Description

Semantic analysis of video has witnessed a significant increase of research activities during the last years Human-centered video analysis plays a central role in this research since humans are the most frequently encountered entities in a video Results of human-centered video analysis can be of use in numerous applications, one of them being multimedia postproduction Three recently devised semantic analysis algorithms are reviewed in this paper

/pdf/human-centered-video-analysis-for-multimedia-postproduction-2b06m1zyu6.pdf

Ioannis Pitas

Papers

Facial expression recognition using shape and texture information

Parallel algorithms and architectures for 2-D butterfly-like transforms

Mosaicing of Flattened Images from Straight Homogeneous Generalized Cylinders

Computational Intelligence Approaches for Digital Media Analysis and Description

Human-centered video analysis for multimedia postproduction