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

The classical grayscale morphological operators erosion and dilation are characterized by strong nonlinearities, a feature that is not desirable in certain image filtering applications. This paper investigates the use of a subclass of nonlinear mean filters, instead of the classical morphological transformation. The nonlinearities of these filters can be hardened or softened progressively, by means of a controlling parameter. The statistical properties of these “soft” morphological filters are investigated. It will be shown that the nonlinear mean filters behave better than or equally well to the grayscale morphological transformations, for certain types of noise.

Soft Morphological Operators Based on Nonlinear L P Mean Operators

Non-negativeMatrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. To achieve an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance, we regard that data inside each class form clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method combines these discriminant criteria as constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space. The developed algorithm has been applied to the facial expression recognition problem and experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.

/pdf/using-subclasses-in-discriminant-non-negative-subspace-guz582it8d.pdf

Using subclasses in discriminant non-negative subspace learning for facial expression recognition

Two novel signal-adaptive nonlinear filters are proposed for speckle reduction in ultrasound B-mode images. The first is a modification of the Signal-Adaptive Median, which uses the Fractal Dimension (FD) as a measure of the local signal activity. The second one is based on the L 2 mean filter which is the maximum likelihood (ML) estimator of a constant signal corrupted by multiplicative Rayleigh noise and uses the above-mentioned measure of local signal activity. The proposed signal adaptive nonlinear filters suppress the speckle noise while preserving the image texture.

Use of Fractal Dimension in Signal Adaptive Filters for Speckle Reduction in Ultrasound B-Mode Images

A web content management system (Web CMS) has been developed to host a corpus of 405 folk songs for media sharing. It is based on Drupal 7. Exploring various modules, the system offers multiple functionalities, including user registration, layered information about each song in different pages, similar content search, friendship relationships between the registered users, favorite lists, etc. Users can visit song pages, listen to the songs, and provide feedback. In addition to the Web CMS, a game with a purpose (GWAP), called Erasitechnis GWAP, has been designed and deployed on Facebook as an enjoyable way encouraging users to enroll and provide music annotations. Erasitechnis GWAP is a combined approach of existing games with a purpose, which focuses on tagging Greek folk music promoting less frequent tags. By doing so, descriptive tags are collected enabling the creation of a fully annotated dataset, which is exploited to train recommendation or autotagging systems.

A game with a purpose for annotating Greek folk music in a web content management system

In this paper a novel view-invariant human identification method is presented. A multi-camera setup is used to capture the human body from different observation angles. Binary body masks from all the cameras are concatenated to produce the so-called multi-view binary masks. These masks are rescaled and vectorized to create feature vectors in the input space. A view-invariant human body representation is obtained by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). Fuzzy vector quantization (FVQ) is performed to associate human body representation with movement representations and linear discriminant analysis (LDA) is used to map movements in a low dimensionality discriminant feature space. Two human identification schemes, a movement-specific and a movement-independent one, are evaluated. Experimental results show that the method can achieve very satisfactory identification rates. Furthermore, the use of more than one movement types increases the identification rates.

/pdf/learning-human-identity-using-view-invariant-multi-view-2wjifiez7z.pdf

Ioannis Pitas

Papers

Soft Morphological Operators Based on Nonlinear L P Mean Operators

Using subclasses in discriminant non-negative subspace learning for facial expression recognition

Use of Fractal Dimension in Signal Adaptive Filters for Speckle Reduction in Ultrasound B-Mode Images

A game with a purpose for annotating Greek folk music in a web content management system

Learning human identity using view-invariant multi-view movement representation