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

Physical quantities referring to angles, like vector direction, color hue etc., are periodic in nature. Due to this periodicity edge detectors proposed for data on the line cannot be used to detect edges in angle-related signals. In this paper we use estimators of circular dispersion to introduce edge detectors for angular signals and discuss their application in edge detection on hue images. Extensions of the notion of quasi-range to circular data are also proposed. These "circular" quasi-ranges have good and user-controlled properties as edge detectors on noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using both qualitative and quantitative criteria.

Edge detection operators for angular data

An inflator (10) for an inflatable vehicle occupant protection device, such as an air bag, includes a housing (12) storing a non-pressurized source (16) of inflation fluid. The source (16) of inflation fluid is an elongated body (16) of ignitable material which, when ignited, generates combustion products including inflation gas and particulate matter. An imperforate tubular wall portion (20) of the housing (12) has an inner surface (66) extending fully along and around the elongated body (16) of ignitable material. The inner surface (66) functions to remove particulate matter from the inflation gas. The housing (12) further contains a tubular filter structure (120) formed of gas-permeable material for removing additional particulate matter from the inflation gas. The tubular filter structure (120) is substantially shorter than the elongated body (16) of ignitable material.

Guest Editors' Introduction: Image Security

In this chapter we will describe new methods for anthropocentric semantic video analysis, and will concentrate our efforts to provide a uniform framework by which media analysis can be rendered more useful for retrieval applications as well as for human–computer interaction based application. The main idea behind anthropocentric video analysis is that a film is to be viewed as an artwork and not as a mere of frames following each others. We will show that this kind of analysis which is a straightforward approach of human perception of a movie can finally produce some interesting results of the overall annotation of a video content. “Anthropos” which is the greek word for “human” show the intent of our proposition to concentrate in humans in a movie. Humans are the most essential part of a movie and thus we track down all important features that we can get from low-level and mid-level feature algorithms such as face detection, face tracking, eye detection, visual speech recognition, 3D face reconstruction, face clustering, face verification and facial expressions extraction. All these algorithms produce results which are stored in an MPEG-7 inspired description scheme set which implements the way humans are connecting those features. Therefore as a results we have a structured information of all features that can be found for a specific human (e.g. actor). As it will be shown in this chapter this approach as a straightforward approach of human perception provides a new way of media analysis in the semantic level.

Anthropocentric Semantic Information Extraction from Movies

In this paper, we propose a new method for kernel optimization in kernel-based dimensionality reduction techniques such as kernel principal component analysis and kernel discriminant analysis. The main idea is to use the graph embedding framework for these techniques and, therefore, by formulating a new minimization problem to simultaneously optimize the kernel parameters and the projection vectors of the chosen dimensionality reduction method. Experimental results are conducted in various datasets, varying from real-world publicly available databases for classification benchmarking to facial expressions and face recognition databases. Our proposed method outperforms other competing ones in classification performance. Moreover, our method provides a systematic way to deal with kernel parameters whose calculation was treated rather superficially so far and/or experimentally, in most of the cases.

A novel dimensionality reduction technique based on kernel optimization through graph embedding

Accurate and plausible 3D face reconstruction remains a difficult problem up to this day, despite the tremendous advances in computer technology and the continuous growth of the applications utilizing 3D face models (e.g. biometrics, movies, gaming). In this paper, a two-step technique for efficient 3D face reconstruction from a set of face images acquired using an uncalibrated camera is presented. Initially, a robust structure from motion (SfM) algorithm is applied over a set of manually selected salient image features to retrieve an estimate of their 3D coordinates. These estimates are further utilized to deform a generic 3D face model, using smoothing splines, and adapt it to the characteristics of a human face.

/pdf/3d-human-face-modelling-from-uncalibrated-images-using-2rfgt3hjma.pdf

Ioannis Pitas

Papers

Edge detection operators for angular data

Guest Editors' Introduction: Image Security

Anthropocentric Semantic Information Extraction from Movies

A novel dimensionality reduction technique based on kernel optimization through graph embedding

3D Human Face Modelling From Uncalibrated Images Using Spline Based Deformation.