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 articles in this special section were focused on the current state of the art as well as emerging trends in the design, development, and deployment of sensing and perception technologies for autonomous and automated driving Such technologies include camera, ultrasound, Global Navigation Satellite System-, lidar-, and radar-based platforms integrat ing signa l processing components to process the acquired data and extract information to be used for recognition, navigation, and situational awareness Despite recent advances in such sensing platforms, the performance of these sensors can be significantly constrained by their quality–cost tradeoff, excessive energy consumpt ion, and inconsistency under varying environmental conditions

/pdf/autonomous-driving-part-1-sensing-and-perception-from-the-4aq7mkkx10.pdf

Autonomous Driving: Part 1-Sensing and Perception [From the Guest Editors]

A novel class of Learning Vector Quantizers (LVQs) based on multivariate order statistics is proposed in order to overcome the drawback that the estimators for obtaining the reference vectors in LVQ do not have robustness either against erroneous choices for the winner vector or against the outliers that may exist in vector-valued observations. The performance of the proposed variants of LVQ is demonstrated by experiments. In the case of marginal median LVQ, its asymptotic properties are derived as well. >

A class of order statistics learning vector quantizers

Introduction to parallel digital image processing

This paper presents a novel approach to the Fourier analysis of multichannel time series. Orthogonal matrix functions are introduced and are used in the definition of multichannel Fourier series of continuous-time periodic multichannel functions. Orthogonal transforms are proposed for discrete-time multichannel signals as well. The discrete-time one-dimensional multichannel transforms proposed in this paper are related to two-dimensional single-channel transforms, notably to the discrete Fourier transform and to the discrete Hartley transform. Therefore, fast algorithms for their computation can be easily constructed. Simulations on the use of discrete multichannel transforms on color image compression have also been performed. >

Discrete multichannel orthogonal transforms

A subspace supervised learning algorithm named discriminant non-negative matrix factorization (DNMF) has been recently proposed for classifying human facial expressions. It decomposes images into a set of basis images and corresponding coefficients. Usually, the algorithm starts with random basis image and coefficient initialization. Then, at each iteration, both basis images and coefficients are updated to minimize the underlying cost function. The algorithm may need several thousands of iterations to obtain cost function minimization. We provide a way to significantly improve the speed of the algorithm convergence by constructing initial basis images that meet the sparseness and orthogonality requirements and approximate the final minimization solution. To experimentally evaluate the new approach, we have applied DNMF using the random and the proposed initialization procedure to recognize six basic facial expressions. While fewer iteration steps are needed with the proposed initialization, the recognition accuracy remains within satisfactory levels.

/pdf/on-the-initialization-of-the-dnmf-algorithm-pkbc0gn3ie.pdf

Ioannis Pitas

Papers

Autonomous Driving: Part 1-Sensing and Perception [From the Guest Editors]

A class of order statistics learning vector quantizers

Introduction to parallel digital image processing

Discrete multichannel orthogonal transforms

On the initialization of the DNMF algorithm