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

This paper presents a novel neural module for enhancing existing fast and lightweight 2D human pose estimation CNNs, in order to increase their accuracy. A baseline stem CNN is augmented by a collateral module, which is tasked to encode global spatial and semantic information and provide it to the stem network during inference. The latter one outputs the final 2D human pose estimations. Since global information encoding is an inherent subtask of 2D human pose estimation, this particular setup allows the stem network to better focus on the local details of the input image and on precisely localizing each body joint, thus increasing overall 2D human pose estimation accuracy. Furthermore, the collateral module is designed to be lightweight, adding negligible runtime computational cost, so that the unified architecture retains the fast execution property of the stem network. Evaluation of the proposed method on public 2D human pose estimation datasets shows that it increases the accuracy of different baseline stem CNNs, while outperforming all competing fast 2D human pose estimation methods. 

Fast Single-Person 2D Human Pose Estimation Using Multi-Task Convolutional Neural Networks

In this paper, we investigate the use of discriminant feature selection techniques in the elastic graph matching (EGM) algorithm. State of the art and novel discriminant dimensionality reduction techniques are used in the node feature vectors in order to extract discriminant features. We illustrate the improvements in performance in frontal face verification using a modified multiscale morphological analysis for forming the node feature vectors. All experiments were conducted in the XM2VTS database.

/pdf/linear-discriminant-feature-selection-techniques-in-elastic-3g1meyl4pm.pdf

Linear discriminant feature selection techniques in elastic graph matching

In this paper we propose a new class of filters called Jordan decomposition filters. They are upper and lower bounds of the well-known max and min filters. Our approach is based on a decomposition scheme by representing bounded variation signals as a difference of two increasing functions. Jordan filters are computed as the difference between the local extrema values of the increasing functions. They are faster than max/min filters. The number of operations per sample is constant, regardless the size of the window filter. The computational complexity of the proposed filters decreases with the number of filtering steps needed in an application. Examples are provided and the application in contrast enhancement is investigated.

Jordan decomposition filters

Current autonomous systems (e.g., self-driving cars, autonomous drones, consumer robots, etc.) can already perform a wide variety of tasks and are predicted to be able to collaboratively assist humans in the near future. Thus, the need for efficient human-robot interaction (HRI) methods is greatly increasing. Gesture recognition is an effective HRI approach, since many robots are equipped with cameras and computer vision algorithms have progressed significantly in recent years, with advanced Deep Neural Networks (DNNs) being able to be executed on-board an autonomous system. However, computational/memory limitations are still significant for embedded AI methods, rendering the increase of DNN accuracy without imposing a penalty on runtime requirements a very important research priority. This paper investigates self-supervised DNN pretraining for a novel pretext task, relying on spatiotemporal video frame compression via tensor decomposition and low-rank approximation, as a means to augment gesture recognition performance, without inducing any runtime overhead during the inference stage. Thus, the method permits the use of less complex and much faster neural architectures that are well-suited to robotics applications and HRI. Quantitative evaluation on a gesture recognition dataset for autonomous Unmanned Aerial Vehicle (UAV) handling demonstrates the effectiveness and real-time performance of the proposed method on embedded AI compute hardware.

Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of ...

Ioannis Pitas

Papers

Fast Single-Person 2D Human Pose Estimation Using Multi-Task Convolutional Neural Networks

Linear discriminant feature selection techniques in elastic graph matching

Jordan decomposition filters

Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction

Big Data Clustering with Kernel k-Means: Resources, Time and Performance