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

A variant of the WEBSOM architecture for information retrieval is proposed in this paper. WEBSOM is based on the self-organizing map that employs a linear LMS adaptation rule for updating the weight vector of each neuron. Accordingly, the weight vector converges asymptotically to the conditional cluster mean of the feature vectors assigned to the class represented by the weight vector of the neuron. We propose to replace the updating rule by employing the marginal median. The objective is to overcome the drawbacks of the standard technique in the presence of outliers in the training set and to use robust estimators of the reference vectors for each class. Experimental results demonstrate a superior performance of the proposed variant against the standard algorithm, in terms of the number of training iterations needed so that the mean square error (i.e., the average distortion) drops to the 1 e of its initial value. We provide precision-recall curves as a measure of the quality of the clustering procedur as well. Both techniques are tested using a corpus that comprises web pages selected over the Internet.

/pdf/mm-websom-a-variant-of-websom-based-on-order-statistics-3qqce7kt9o.pdf

Mm-websom: a variant of websom based on order statistics

Vector fields play an important role in the area of color image processing and image sequence processing. Certain classes of vector fields can be handled more efficiently in a polar coordinate system. Optimal L-filters for filtering of vector magnitude are derived. The principles of directional data are presented and the notion of ordering is extended to angular data in order to filter and analyze the vector direction.

Application of directional statistics to nonlinear vector field filtering and analysis

In this paper, we present a novel method for template simplification, where the template is used to find interesting objects within an image. In this way, we improve computational performance since less template points are matched using a simplified template. Moreover, we increase the reliability of the matching as we keep template points with focusing on the main shape behavior (skeleton) of the template. The theoretical background of the simplification is derived through the centroidal Voronoi tessellation framework. The efficiency of the proposed approach is demonstrated with detecting human appearance in thermal images.

/pdf/object-simplification-using-a-skeleton-based-weight-function-3a6icpn3lb.pdf

Object Simplification Using a Skeleton-Based Weight Function

In this paper we propose a virtual drilling algorithm which is applied on 3-D objects. We consider that initial we are provided with a sparse set of parallel and equi-distant slices of a 3-D object. We propose a volumetric interpolation algorithm for recovering the 3-D shape from the given set of slices. This algorithm employs a morphology morphing transform. Drilling is simulated on the resulting volume as a 3-D erosion operation. The proposed technique is applied for virtual drilling of teeth considering various burr shapes as erosion elements.

/pdf/virtual-drilling-in-3-d-objects-reconstructed-by-shape-based-1n7huju31t.pdf

Virtual Drilling in 3-D Objects Reconstructed by Shape-Based Interpolation

This chapter explains the theory of Principal component analysis (PCA) in detail and presents practical implementation issues along with various application examples. It introduces the mathematical concepts behind PCA such as mean value, covariance, eigenvalues, and eigenvectors. The principal components are ordered in way that the principal components retain information in descending order. In many cases, PCA is implemented using the singular value decomposition (SVD) of the covariance matrix. The standard PCA algorithm can be extended to support nonlinear principal components using nonlinear kernels. SVD is an important tool for factorizing an arbitrary real or complex matrix, with many applications in various research areas, such as signal processing and statistics. PCA can also be performed without eigenanalysis. This can be achieved by using neural networks that can be trained to extract the principal components of the training set.

Ioannis Pitas

Papers

Mm-websom: a variant of websom based on order statistics

Application of directional statistics to nonlinear vector field filtering and analysis

Object Simplification Using a Skeleton-Based Weight Function

Virtual Drilling in 3-D Objects Reconstructed by Shape-Based Interpolation

Principal Component Analysis