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

In this paper, we propose a new method for facial expression recognition. We utilize the Candide facial grid and apply Principal Components Analysis (PCA) to find the two eigenvectors of the model vertices. These eigenvectors along with the barycenter of the vertices are used to define a new coordinate system where vertices are mapped. Support Vector Machines (SVMs) are then used for the facial expression classification task. The method is invariant to in-plane translation and rotation as well as scaling of the face and achieves very satisfactory results.

A model-based facial expression recognition algorithm using Principal Components Analysis

In this paper, a novel class of support vector machines (SVM) is introduced to deal with facial expression recognition. The proposed classifier incorporates statistic information about the classes under examination into the classical SVM. The developed system performs facial expression recognition in facial videos. The grid tracking and deformation algorithm used tracks the Candide grid over time as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of Candide nodes is used as an input to the bank of novel SVM classifiers, that are utilized to recognize the six basic facial expressions. The experiments on the Cohn-Kanade database show a recognition accuracy of 98.2%.

/pdf/facial-expression-recognition-in-videos-using-a-novel-multi-npt4jlb34h.pdf

Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant

In this paper, statistical analysis of watermarking schemes based on correlation detection is presented. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are exploited, resulting in superior watermark detection reliability. Correlation/spectral properties of such sequences are easily controllable, a fact that reflects on the watermarking system performance.

https://www.researchgate.net/profile/Vassilios_Solachidis/publication/221379439_Statistical_analysis_of_Markov_chaotic_sequences_for_watermarking_applications/links/09e4150ca1477d19a1000000.pdf

Statistical analysis of Markov chaotic sequences for watermarking applications

This paper introduces, a virtual teeth drilling system named Virtual Dental Patient designed to aid dentists in getting acquainted with the teeth anatomy, the handling of drilling instruments and the challenges associated with the drilling procedure. The basic aim of the system is to be used for the training of dental students. The application features a 3D model of the face and the oral cavity that can be adapted to the characteristics of a specific person and animated. Drilling using a haptic device is performed on realistic teeth models (constructed from real data), within the oral cavity. Results and intermediate steps of the drilling procedure can be saved for future use.

/pdf/virtual-dental-patient-a-system-for-virtual-teeth-drilling-16ondw61e7.pdf

Virtual Dental Patient: a System for Virtual Teeth Drilling

Audiovisual coverage of sports events using Unmanned Aerial Vehicles (UAVs) is becoming increasingly popular. Intelligent audiovisual (A/V) shooting tools, accurately identifying the 2D region of cinematographic attention (RoCA) depicting rapidly moving target ensembles and automatically controlling the UAVs/cameras through visual content analysis, are thus needed. A novel algorithmic pipeline is proposed, implementing computational UAV cinematography for assisting sports coverage, based on semantic, human-centered visual analysis. Athlete and ball detection / tracking results as well as their spatial distribution on the image plane are the semantic features extracted from UAV video feed and exploited for RoCA extraction, based solely on present and past target detections. A PID controller visually controlling a real or virtual camera to track the RoCA and produce aesthetically pleasing shots, without exploiting 3D location-related information, is employed. The proposed method is evaluated on actual UAV footage from soccer matches and promising results are obtained.

Ioannis Pitas

Papers

A model-based facial expression recognition algorithm using Principal Components Analysis

Facial Expression Recognition in Videos using a Novel Multi-Class Support Vector Machines Variant

Statistical analysis of Markov chaotic sequences for watermarking applications

Virtual Dental Patient: a System for Virtual Teeth Drilling

Computational UAV Cinematography for Intelligent Shooting Based on Semantic Visual Analysis