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

In this paper, a technique for temporal embedding of a multibit watermark on an audio signal is proposed. The method is based on generating a chaotic sequence which is used for modifying the audio samples. A chaotic sequence generates the watermark, while the parameters of the embedding procedure are chosen so as to minimize the perceivable distortion of the initial signal, while preserving the power of the watermark at a detectable level. The watermark is resistant to lowpass manipulation attacks, such as filtering or compression, and cropping attacks as well.

Robust multibit audio watermarking in the temporal domain

A mutual information based articulated object tracking scheme is proposed in this paper Articulation constraints are introduced using a kinematic model Further constraints are introduced based on the human joint anatomy and flexibility The tracking scheme is enhanced by using the tracked object texture map image The tracking history is incorporated in the tracking scheme by using a temporal model or a Kalman filtering scheme The Kalman filtering scheme greatly enhances the tracking scheme provided the suitable initial conditions are set The resulting system was tested on arm and finger tracking cases using real image sequences

/pdf/a-mutual-information-approach-to-articulated-object-tracking-1j4b36agdq.pdf

A mutual information approach to articulated object tracking

Randomized Hough transform (RHT) has been recently proposed as a new and efficient variation of the Hough transform for curve detection. In this paper the RHT is combined with the fuzzy cell Hough transform (FCHT) and a new variation, the randomized fuzzy cell Hough transform (RFCHT) is proposed. The p-dimensional parameter space of Hough transform is split into fuzzy cells with overlapped intervals of confidence. The fuzzy cells are defined as fuzzy numbers. The RFCHT selects p pixels from an edge image by random sampling and solves the p parameters of a curve. Then the p parameters accumulate to more than one fuzzy cells, since the fuzzy cells intervals are overlapped by adding a value that belongs in the interval [0, 1] and is calculated from the membership function of the corresponding fuzzy cell. The procedure continues by using a percentage of the total contour pixels. The RFCHT algorithm preserves the advantages of RHT and FCHT. The algorithm has good computational speed and small storage requirements due to random sampling and correct and more accurate detections, especially in noisy images, due to fuzzy cells.

Randomized fuzzy cell Hough transform

Various anthropometric studies have been conducted in the last decade in order to investigate how different physiological or behavioral human characteristics can be used as identity evidence to prove the individuality of each person. Some of these characteristics are: face, eyes, ears, teeth, fingers, hands, feet, veins, voice, signature, typing style and gait. Since the first biometric security systems appeared in the market, an increasing demand for novel techniques that will cover all different scenarios, has been observed. Every new method appears to outmatch some of its competitors but, at the same time, presents disadvantages compared to others. However, there is still no method that consists a single panacea to all different scenarios and demands for security. This is the reason for which researchers are on a continuous effort for more efficient and generic biometric modalities that can be used in various applications. In this chapter, emerging biometric modalities that appeared in the last years in order to improve the performance of biometric recognition systems, are presented. The presented methods are divided in two major categories, intrusive and non-intrusive ones, according to the level of user nuisance that each system sets off.

Intelligent Multimedia Analysis for Emerging Biometrics

In this paper we study the stability of support vector machines in face detection by decomposing their average prediction error into the bias, variance, and aggregation effect terms. Such an analysis indicates whether bagging, a method for generating multiple versions of a classifier from bootstrap samples of a training set, and combining their outcomes by majority voting, is expected to improve the accuracy of the classifier. We estimate the bias, variance, and aggregation effect by using bootstrap smoothing techniques when support vector machines are applied to face detection in the AT & T face database and we demonstrate that support vector machines are stable classifiers. Accordingly, bagging is not expected to improve their face detection accuracy.

Ioannis Pitas

Papers

Robust multibit audio watermarking in the temporal domain

A mutual information approach to articulated object tracking

Randomized fuzzy cell Hough transform

Intelligent Multimedia Analysis for Emerging Biometrics

On the stability of support vector machines for face detection