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

Video shot boundary detection using singular value decomposition

Storage, browsing and analysis of human activity videos can be significantly facilitated by automated video summarization. Unsupervised key-frame extraction remains the most widely applicable technique for summarizing activity videos. However, their specific properties make the problem difficult to solve. Typical relevant algorithms fall under the video frame clustering or the dictionary-of-representatives families, with salient dictionary learning having been recently proposed. Under this formulation, the video frames selected as key-frames are the ones which simultaneously best reconstruct the entire video and are salient compared to the rest. This paper improves upon such a method by replacing the video frame saliency estimation term with one based on Regularized SVD-based Low Rank Approximation, taking advantage of the well-established correlation between midrange matrix singular values and salient regions. Extensive empirical evaluation showcases the high performance of both the salient dictionary learning framework and the specific proposed method.

Regularized Svd-Based Video Frame Saliency for Unsupervised Activity Video Summarization

http://poseidon.csd.auth.gr/papers/PUBLISHED/JOURNAL/pdf/2013/PR_SVM_2013.pdf

Using robust dispersion estimation in support vector machines

In this paper, a novel supervised feature extraction method is presented. The method employs discriminant analysis in the features derived by non-negative matrix factorization (NMF). In this way, a two phase discriminant feature extraction procedure is implemented, namely NMF plus linear discriminant analysis (LDA). The introduced method has been applied to the problem of frontal face verification using the well known XM2VTS database, where a better performance than NMF, eigenfaces and Fisherfaces has been achieved

/pdf/discriminant-nmffaces-for-frontal-face-verification-39y36ftxbr.pdf

Discriminant NMFfaces for Frontal Face Verification

A new adaptive filter structure is proposed which is based on linear combinations of order statistics (called adaptive L-filter). An efficient method to update the filter coefficients is presented, which is based on the least mean square error criterion. The proposed filter can adapt well to a variety of noise probability distributions, including impulsive noise. It also performs well in the case of nonstationary signals and, therefore, it is suitable for image processing applications. >

Ioannis Pitas

Papers

Video shot boundary detection using singular value decomposition

Regularized Svd-Based Video Frame Saliency for Unsupervised Activity Video Summarization

Using robust dispersion estimation in support vector machines

Discriminant NMFfaces for Frontal Face Verification

Adaptive L-filters