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

1D, 2D and multidimensional convolutions are basic tools in deep learning, notably in convolutional neural networks (CNNs) and in computer vision (template matching, correlation trackers). Therefore, fast 1D/2D/3D convolution algorithms are essential for advanced machine learning and computer vision. This paper presents: 1) novel optimal n-D cyclic convolution algorithms having minimal multiplicative complexity that are much faster than any competing convolution algorithm internationally and 2) methods for speeding up such optimal convolution algorithms on GPUs and multicore CPUs. Such a speedup is very important both for CNN training and CNN testing, particularly in embedded environments (e.g., on drones) and real-time applications (e.g., fast CNN inference for object detection and correlation trackers for embedded real-time object tracking).

Optimal Multidimensional Cyclic Convolution Algorithms For Deep Learning And Computer Vision Applications

A joint probabilistic face detection and tracking algorithm for combining a likelihood estimation and a prior probability is proposed. Face tracking is achieved by a Bayesian framework. The likelihood estimation scheme is based on statistical training of sets of automatically generated feature points, while the prior probability estimation is based on the fusion of an information theoretic tracking cue and a Gaussian temporal model. The likelihood estimation process is the cone of a multiple face detection scheme used to initialize the tracking process. The resulting system was tested on real image sequences and is robust to significant partial occlusion and illumination changes.

An information theoretic approach to joint probabilistic face detection and tracking

A novel morphological dynamic link architecture that employs morphological shape decomposition as a feature extraction mechanism has been developed and tested. The first experimental results collected are very encouraging and indicate that the proposed method outperforms the (standard) dynamic link matching that is based on Gabor wavelets.

Face authentication based on morphological shape decomposition

In this paper a new variation of Hough transform is proposed. The parameter space of Hough transform is recursively roughly split into fuzzy cells which are defined as fuzzy numbers. This fuzzy partition of the parameter space provides the advantage to use the uncertainty of the contour points location, which is increased when noisy images have to be used. It can be used to detect contours in an image, with better accuracy, especially in noisy images. Moreover, the regions of the parameter space having no contours are rejected during the iterations. As a result the computation time is significantly decreased.

Introducing the select and split fuzzy cell Hough transform

This paper presents a parallel algorithm for the calculation of vector median filters on mesh connected computers. A straightforward algorithm to find the vector median requires the computation of the sum of the distances (norms) of each vector to all other vectors in the filter window. By taking into account the fact that adjacent filter windows share common input samples, the computation of the common norms of adjacent windows could be done only once. In the proposed algorithm, a 2-D signal is assumed to be stored in a SIMD mesh connected computer of the same size. According to this scheme, each processing element (PE) computes some norms and the rest ones, that are required for the selection of the vector median, are received from its neighbouring PEs via the local communication channels. >

Ioannis Pitas

Papers

Optimal Multidimensional Cyclic Convolution Algorithms For Deep Learning And Computer Vision Applications

An information theoretic approach to joint probabilistic face detection and tracking

Face authentication based on morphological shape decomposition

Introducing the select and split fuzzy cell Hough transform

Two-dimensional vector median filters on mesh connected computers