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

Digital products are easy to copy, reproduce, and maliciously process in a network environment Over the past decade, watermarking has emerged as an important technology for protecting copyright of multimedia products We present a method for embedding and detecting watermarks in vector graphics images containing polygonal lines, and present results from simulated attacks Our proposed algorithm embeds watermarks in polygonal lines describing image contours These contours are usually described in vector format Our watermarking method slightly modifies the vertex coordinates of the polygonal line We embed the watermark in the magnitude of the curve's Fourier descriptors to exploit its location, scale, and rotation invariant properties Our technique has certain similarities to bitmap image watermarking in the discrete Fourier transform (DFT) domain However, it's essentially different because it can be applied to vector rather than bitmap images Furthermore, Fourier descriptor watermarking problems differ from DFT bitmap image watermarking problems

Watermarking polygonal lines using Fourier descriptors

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network’s output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behavior analysis, i.e., the recognition of human face, facial expression, and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.

/pdf/graph-embedded-extreme-learning-machine-37ieiuthhi.pdf

Graph Embedded Extreme Learning Machine

A content-based video parsing and indexing method is presented in this paper, which analyzes both information sources (auditory and visual) and accounts for their inter-relations and synergy to extract high-level semantic information. Both frame- and object-based access to the visual information is employed. The aim of the method is to extract semantically meaningful video scenes and assign semantic label(s) to them. Due to the temporal nature of video, time has to be accounted for. Thus, time-constrained video representations and indices are generated. The current approach searches for specific types of content information relevant to the presence or absence of speakers or persons. Audio-source parsing and indexing leads to the extraction of a speaker label mapping of the source over time. Video-source parsing and indexing results in the extraction of a talking-face shot mapping over time. Integration of the audio and visual mappings constrained by interaction rules leads to higher levels of video abstraction and even partial detection of its context.

Content-based video parsing and indexing based on audio-visual interaction

Low Level Parallel Image Processing Parallel FFT-like Transform Algorithms on Transputers Parallel Edge Detection and Related Algorithms Parallel Segmentation Algorithms MIMD and SIMD Parallel Range Data Segmentation Parallel Stereo and Motion Estimation Parallel Implementations of the Backpropagation Learning Algorithm Based on Network Topology Parallel Neural Computation Based on Algebraic Partitioning Parallel Neural Computing Based on Network Duplicating PARALLEL EIKONA: A Parallel Digital Image Processing Package Parallel Architectures and Algorithms for Real Time Computer Vision Index.

Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks

In this paper we describe a general framework for image copyright protection through digital watermarking In particular we present the main features of an efficient watermarking scheme, discuss robustness issues and describe the three main stages of a watermarking algorithm namely watermark generation, embedding and detection

Ioannis Pitas

Papers

Watermarking polygonal lines using Fourier descriptors

Graph Embedded Extreme Learning Machine

Content-based video parsing and indexing based on audio-visual interaction

Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks

Digital watermarking: An overview