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.

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Detecting faces in images: a survey

The exponential growth of video data produced by surveillance cameras, cell phones and movie post-production creates the need to process big-data using methods that are able to produce instantaneous result. Video summarization can be accomplished and represented in several manners. The achieved summaries might be a sequence of images or short videos. In our method, an input video is divided into segments. From each segment we calculate key frames using three different key frame definitions, to summarize the video data. The contribution of this paper is to describe how to incorporate techniques that extract on the fly results.

Visual Information Analysis for Big-Data Using Multi-core Technologies

In this paper, the Fuzzy Vector Median is proposed, defined as an extension of Vector Median. It is based on a novel distance definition of multidimensional fuzzy numbers (fuzzy vectors), which satisfy the property of angle decomposition. The proposed distance of two fuzzy vectors depends on the classical distance of the fuzzy set centers and on the fuzziness that every fuzzy set holds. As a result the Fuzzy Vector Median of a set of fuzzy vectors is affected by the presence of fuzziness.

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Fuzzy vector median definition based on a fuzzy vector distance

The noise in musical audio recordings is assumed to obey an α-stable distribution. A sparse linear regression framework with structured priors is elaborated. Markov Chain Monte Carlo is used to infer the clean music signal model and the α-stable noise distribution parameters. The musical audio recordings are processed both as a whole and in segments by using a sine-bell window for analysis and overlap-and-add reconstruction. Experiments on noisy Greek folk music excerpts demonstrate better denoising under the α-stable noise assumption than the Gaussian white noise one, and when processing is performed in segments rather than in full recordings.

/pdf/greek-folk-music-denoising-under-a-symmetric-a-stable-noise-8d59v4ukkk.pdf

Greek folk music denoising under a symmetric α-stable noise assumption

Digital implementations of sorting networks that rely on a digital signal processor core are not as efficient as their analog counterparts. This paper builds on the L/sub p/ comparators for which efficient analog implementations exist that employ operational amplifiers. From a statistical point of view, L/sub p/ comparators are based on nonlinear means. Their probability density function and the first- and second-order moments are derived for independent uniformly distributed inputs. L/sub p/ comparators provide estimates of the minimum and maximum of their inputs. A proper approach to compensate for the estimation errors is proposed. Applications of the L/sub p/ comparators in odd-even transposition networks, median approximation networks, and min/max networks are presented.

/pdf/sorting-networks-using-l-sub-p-mean-comparators-for-signal-1jsmxqhzb2.pdf

Sorting networks using L/sub p/ mean comparators for signal processing applications

In this paper, we propose a framework for clustering shots from stereoscopic videos into clusters that correspond to semantic concepts exploiting visual and disparity information. Various color, disparity and texture descriptors are applied to shot key frames for obtaining low-level representations. Self Organizing Maps are subsequently employed upon various combinations of these representations in order to determine a lattice of representative semantic concepts. Experimental results on performances and football stereoscopic videos show that the use of disparity information leads to better clustering compared to using visual information only.

/pdf/stereoscopic-video-shot-clustering-into-semantic-concepts-52hp9r7p5p.pdf

Ioannis Pitas

Papers

Visual Information Analysis for Big-Data Using Multi-core Technologies

Fuzzy vector median definition based on a fuzzy vector distance

Greek folk music denoising under a symmetric α-stable noise assumption

Sorting networks using L/sub p/ mean comparators for signal processing applications

Stereoscopic video shot clustering into semantic concepts based on visual and disparity information