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 an algorithm for face tracker's trajectories clustering. Our approach is based on the mutual information of the images and more precisely its normalized version (NMI). We make use of 2 color channels from the HSV space (hue and saturation) in order to calculate a 4D joint histogram and therefore calculate the mutual information. In this paper we also develop an algorithm where we apply robust heuristics and make use of a tracker information in order to diminish dimensionality and augment accuracy of our results. It is a supervised clustering algorithm which is therefore used (fuzzy c-means) in order to gather same trajectories and same faces together.

A Face Tracker Trajectories Clustering Using Mutual Information

Virtual Dental Patient: a 3D Oral Cavity Model and its Use in Haptics-based Virtual Reality Cavity Preparation Inendodontics

Image fingerprinting systems aim to extract unique and robust image descriptors (in analogy to human fingerprints). They search for images that are not only perceptually similar but replicas of an image generated through mild image processing operations. In this paper, we examine the use of color descriptors based on a 24-color quantized palette for image fingerprinting. Comparisons are provided between different similarity measures methods as well as regarding the use of color-only and spatial chromatic histograms.

/pdf/color-features-for-image-fingerprinting-3corad1zuq.pdf

Color features for image fingerprinting

In this paper spectral clustering techniques are implemented and evaluated on image clustering for single channel and 3D video analysis. The main idea is to use mutual information to create a similarity matrix for image pairs and then apply spectral clustering. Then, spectral clustering techniques can be used for image clustering. Such clustering techniques are then extended to stereo video. The application at hand includes facial image clustering on single view and stereo videos facial images.

Facial image clustering in single channel and stereo video content

Visual Object Tracking remains an open and challenging task in the Computer Vision field, requiring tracking algorithms to achieve a feeble balance between precision and speed performance. In this work, inspired by the classic Mean Shift algorithm for object tracking using histograms, as well as the recent advances of deep Convolutional Neural Networks (CNNs), we propose a novel tracker that incorporates elements from both worlds. Our tracker uses a deep CNN as the feature extraction backbone, which is capable of extracting semantically meaningful features from the target and its background, as well as a fully learnable Bag-of-Features mechanism which extracts histograms from those features. The tracker operates in a fully-convolutional fashion, allowing for the direct and efficient evaluation of multiple possible target locations. Extensive experimental results demonstrate the efficiency and effectiveness of the proposed tracker, allowing it to run at high speeds even on systems with lower computational capacity.

Ioannis Pitas

Papers

A Face Tracker Trajectories Clustering Using Mutual Information

Virtual Dental Patient: a 3D Oral Cavity Model and its Use in Haptics-based Virtual Reality Cavity Preparation Inendodontics

Color features for image fingerprinting

Facial image clustering in single channel and stereo video content

Deep Convolutional Feature Histograms for Visual Object Tracking