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.

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Detecting faces in images: a survey

Recently, much research has been done on how to secure personal data, notably facial images. Face de-identification is one example of privacy protection that protects person identity by fooling intelligent face recognition systems, while typically allowing face recognition by human observers. While many face de-identification methods exist, the generated de-identified facial images do not resemble the original ones. This paper proposes the usage of adversarial examples for face de-identification that introduces minimal facial image distortion, while fooling automatic face recognition systems. Specifically, it introduces P-FGVM, a novel adversarial attack method, which operates on the image spatial domain and generates adversarial de-identified facial images that resemble the original ones. A comparison between P-FGVM and other adversarial attack methods shows that P-FGVM both protects privacy and preserves visual facial image quality more efficiently.

Adversarial Face De-Identification

Digital image processing and analysis can be an important tool for the restoration of works of art. This paper presents three applications of image processing in this field: a method for digital crack restoration of paintings, a technique for color restoration of old paintings and a method for mosaicing of partial images of works of art painted on curved surfaces. A digital archiving system for works of arts is also described.

/pdf/digital-image-processing-in-painting-restoration-and-341nn6ivko.pdf

Digital image processing in painting restoration and archiving

Facial Expression Analysis and Synthesis: A Survey

This letter proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal estimator (SNE), which is designed for structured range sensor data, e.g. , depth/disparity images. 3F2N SNE computes surface normals by simply performing three filtering operations (two image gradient filters in horizontal and vertical directions, respectively, and a mean/median filter) on an inverse depth image or a disparity image. Despite the simplicity of 3F2N SNE, no similar method already exists in the literature. To evaluate the performance of our proposed SNE, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3D mesh models, each of which is used to generate 1800–2500 pairs of depth images (resolution: 480 × 640 pixels) and the corresponding ground-truth surface normal maps from different views. 3F2N SNE demonstrates the state-of-the-art performance, outperforming all other existing geometry-based SNEs, where the average angular errors with respect to the easy, medium and hard datasets are 1.66 $\boldsymbol{^\circ }$ , 5.69 $\boldsymbol{^\circ }$ and 15.31 $\boldsymbol{^\circ }$ , respectively. Furthermore, our C++ and CUDA implementations achieve a processing speed of over 260 Hz and 21 kHz, respectively. Our datasets and source code are publicly available at sites.google.com/view/3f2n .

Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

Propagation front or grassfire methods are very popular in image processing because of their efficiency and because of their inherent geodesic nature. However, because of their random-access nature, they are inefficient in large images that cannot fit in available random access memory. We explore ways to increase the memory efficiency of two algorithms that use propagation fronts: the skeletonization by influence zones and the watershed transform. Two algorithms are presented for the skeletonization by influence zones. The first computes the skeletonization on surfaces without storing the enclosing volume. The second performs the skeletonization without any region reference, by using only the propagation fronts. The watershed transform algorithm that was developed keeps in memory the propagation fronts and only one greylevel of the image. All three algorithms use much less memory than the ones presented in the literature so far. Several techniques have been developed in this work in order to minimize the effect of these set operations. These include fast search methods, double propagation fronts, directional propagation, and others.

Ioannis Pitas

Papers

Adversarial Face De-Identification

Digital image processing in painting restoration and archiving

Facial Expression Analysis and Synthesis: A Survey

Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator

Memory efficient propagation-based watershed and influence zone algorithms for large images