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

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Video Shot Detection and Condensed

Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been accomplished on Graph Embedding (GE), which is a general framework unifying several subspace learning techniques. In this paper, GE has been extended in order to integrate subclass discriminant information resulting to the novel Subclass Graph Embedding (SGE) framework. The kernelization of SGE is also presented. It is shown that SGE comprises a generalization of the typical GE including subclass DR methods. In this context, the theoretical link of SDA and CDA methods with SGE is established. The efficacy and power of SGE has been substantiated by comparing subclass DR methods versus a diversity of unimodal methods all pertaining to the SGE framework via a series of experiments on various real-world data.

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Graph Embedding Exploiting Subclasses

In this paper, we describe the Artificial Intelligence and Information Analysis (AIIA) laboratory approach for shot boundary detection as applied to the TRECVID 2006 video retrieval benchmark. The paper describes the approach as well as the performance analysis. The method relies on evaluating mutual information between multiple pairs of frames within a certain temporal window. The performance of the method on the benchmark data was in general very satisfactory.

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AIIA shot boundary detection at TRECVID 2006.

A general approach for residual representation of one-dimensional (1-D) and two-dimensional (2-D) signals is defined. Signals are reconstructed as a sum of components that are recursively determined by using a constructive transform. Several morphological constructive transforms are proposed and their corresponding representations are discussed. The use of residual representations in signal and image compression is investigated with promising results.

Morphological residual representations of signals

Movies constitute a large portion of the entertainment industry, as over 9.000 hours of video are released every year [21]. As the bandwidth available to users increases, online movie stores – the equivalent of popular digital music stores – are emerging. They provide users an opportunity to build large personal movie repositories. The convenience of digital movie repositories will be in doubt, unless multimedia data management is employed for organizing, navigating, browsing, searching, and viewing multimedia content. Semantic content-based video indexing offers a promising solution for efficient digital movie management. Semantic video indexing aims at extracting, characterizing, and organizing video content by analyzing the visual, aural, and textual information sources of video. The need for content-based audiovisual analysis has been realized by the MPEG committee, leading to the creation of the MPEG-7 standard [16]. The current approaches for automatic movie analysis and annotation mostly focus on the visual information, while the aural information receives little or no attention. However, the integration of the aural information with the visual one can improve semantic movie content analysis. The predominant approach to semantic movie analysis is to initially extract some low-level audiovisual features (such as color and texture from images or energy and pitch from audio), derive some mid-level entities (such as video shots, keyframes, appearance of faces and audio classes), and finally understand video semantic content by analyzing and combining these entities. A hierarchical video indexing structure is displayed in Fig. 7.1. Movie analysis aims at obtaining a structured organization of the movie content and understanding its embedded semantics like humans do. It has been handled in different ways, depending on the analysis level and the assumptions on the film syntax described in Section 7.1. Most movie analysis efforts concentrate on movie scene or shot detection, while other works focus on the separation of dialogue and non-dialogue scenes. Several efforts have been made for dialogue scene detection, some efforts have concentrated to

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Ioannis Pitas

Papers

Video Shot Detection and Condensed

Graph Embedding Exploiting Subclasses

AIIA shot boundary detection at TRECVID 2006.

Morphological residual representations of signals

Movie Analysis with Emphasis to Dialogue and Action Scene Detection