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

In this paper, a new high-resolution model of a virtual human head is introduced, aiming to build an educational tool for anatomy studies of the head, oral/nasal cavities and teeth. The human head model is based on anatomical data provided by Visible Human Project. Firstly, anatomical structures' contours were extracted on head slices in a semi-automatic way. Secondly, morphological operations were applied in the extracted contours for smoothing the anatomical structure volume. For anatomical structure surface extraction, Discretized Marching Cubes were employed. One hundred thirteen head anatomical structures have been segmented and modeled. The basic aim of the created head model is to be used for head anatomy training and for creating virtual medical/dental patients.

PROSOPON: A virtual anatomical 3D head model

This paper presents a new hierarchical, modular and scalable mesh model of the human head, neck and oral cavity created by using anatomical information and computerized tomography (CT) data taken from the Visible Human Project. The described model, which is an extension of the MPEG-4 head model, covers the full geometry of the back of the head and the main organs of the oral cavity. The modular nature of the model makes it adaptable as a whole or per module, to any corresponding data of a specific human by means of a Finite Element Method (FEM). Our publicly available model can be used for creating virtual dental patient models as well as in other related applications in medicine, phonetics etc.

A Virtual Anatomical 3D Head, Oral Cavity and Teeth Model for Dental and Medical Applications

Three adaptive multichannel L-filters based on marginal data ordering are proposed. They rely on well-known algorithms for the iterative minimization of the mean square error (MSE), namely, the least mean squares (LMS), the normalized LMS (NlMs), and the LMS- Newton (LMSN) algorithms. We treat both the unconstrained minimization of the MSE and the minimization of the MSE when structural constraints are imposed on the filter coefficients. The performance of the proposed adaptive multichannel L-filters is compared to that of other multivariate nonlinear filters in color image filtering. Adaptive multichannel linear filters and adaptive single-channel L-filters are considered as well. Performance comparisons are made in both RGB and U*V*W* color spaces. The proposed adaptive multichannel L-filters outperform the other candidates in noise suppression for color images corrupted by mixed impulsive and additive white contaminated Gaussian noise.

Adaptive multichannel marginal L-filters

Salient dictionary learning has recently proven to be effective for unsupervised activity video summarization by key-frame extraction. All relevant methods select a small subset of the original data points/video frames as dictionary atoms/representatives that, in concert, both optimally reconstruct the original entire dataset/video sequence and are salient. Therefore, they attempt to simultaneously optimize a reconstruction term, pushing towards a dictionary/summary that best reconstructs the entire dataset, and a saliency term, pushing towards a dictionary composed of salient data points. In this paper, a hypothesis is proposed and empirically tested, namely that more salient data points can be obtained by attempting to restrain reconstruction error separately for each original data point. Thus, salient dictionary learning is extended by adding a third term to the objective function, pushing towards optimal point reconstruction. A pre-existing greedy, iterative algorithm for salient dictionary learning is modified according to the proposed extension in two alternative ways. The resulting methods achieve state-of-the-art performance in three databases, verifying the validity of our hypothesis.

Greedy salient dictionary learning with optimal point reconstruction for activity video summarization

In this paper, a robust blind watermarking method for 3D volumes is presented. A bivalued watermark is embedded in the Fourier transform magnitude of the 3D volume. The Fourier domain has been selected because of its important properties in terms of scaling and rotation invariance. Furthermore, a special symmetry of the watermark is exploited, in order to decrease the detection time. The proposed method is proven to be resistant to 3D lowpass filtering, noise addition, scaling, translation, cropping and rotation. Experimental results prove the robustness of this method against the above-mentioned attacks.

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Ioannis Pitas

Papers

PROSOPON: A virtual anatomical 3D head model

A Virtual Anatomical 3D Head, Oral Cavity and Teeth Model for Dental and Medical Applications

Adaptive multichannel marginal L-filters

Greedy salient dictionary learning with optimal point reconstruction for activity video summarization

Watermarking digital 3D volumes in the discrete Fourier transform domain