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

Gimbal Control for Vision-based Target Tracking

3D geometric split–merge segmentation of brain MRI datasets

This paper presents a new learning algorithm for radial basis functions (RBF) neural network, based on robust statistics. The extention of the learning vector quantizer for second order statistics is one of the classical approaches in estimating the parameters of a RBF model. The paper provides a comparative study for these two algorithms regarding their application in probability density function estimation. The theoretical bias in estimating one-dimensional Gaussian functions are derived. The efficiency of the algorithm is shown in modelling two-dimensional functions. >

/pdf/robust-estimation-for-radial-basis-functions-1ns19jd3s0.pdf

Robust estimation for radial basis functions

The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study. 

/pdf/public-opinion-monitoring-through-collective-semantic-r8q38828.pdf

Public opinion monitoring through collective semantic analysis of tweets

In this paper, the robustness of appearance-based, subspace learning techniques for facial expression recognition in geometrical transformations is explored. A plethora of facial expression recognition algorithms is presented and tested using three well-known facial expression databases. Although, it is common-knowledge that appearance based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature and the problem is considered, a priori, solved. However, when it comes to automatic real-world applications, inaccuracies are expected, and a systematic preprocessing is needed. After a series of experiments we observed a strong correlation between the performance and the bounding box position. The mere investigation of the bounding box's optimal characteristics is insufficient, due to the inherent constraints a real-world application imposes, and an alternative approach is demanded. Based on systematic experiments, the database enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of subspace techniques for facial expression recognition.

/pdf/improving-the-robustness-of-subspace-learning-techniques-for-25d88ro9tq.pdf

Ioannis Pitas

Papers

Gimbal Control for Vision-based Target Tracking

3D geometric split–merge segmentation of brain MRI datasets

Robust estimation for radial basis functions

Public opinion monitoring through collective semantic analysis of tweets

Improving the robustness of subspace learning techniques for facial expression recognition