A Human identification technique using images of the iris and wavelet transform

A new approach for recognizing the iris of the human eye is presented. Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1-D) signals are compared with model features using different dissimilarity functions.

Sensationalizing death? Graphic disaster images in the tabloid and broadsheet press

Debates over the extent of graphic imagery of death in newspapers often suffer from generalized assertions that are based on inadequate or incomplete empirical evidence. Newspapers are believed to display death in very graphic ways, with particularly the tabloid press assumedly leading a race to the bottom. This article reports the results of a study of tabloid and broadsheet images of death from the 2010 Haiti earthquake in eight Western European and North American countries. It shows that, far from omnipresent, graphic images of death are relatively rare. While tabloids overall display a larger percentage of graphic images, this was not the case everywhere, with particularly the UK, Canada and the US displaying strong similarities between tabloids and broadsheets. In Austria, Germany, Norway and Switzerland, on the other hand, there were distinct differences between the two types. The article argues that different extents of tabloidization may account for these differences.

Detecting and Analyzing Linear Structures in Biomedical Images : A Case Study Using Corneal Nerve Fibers

Diabetic peripheral neuropathy (DPN) is one of the most common long-term complications of diabetes. The accurate detection and quantification of DPN are important for defining at-risk patients, anticipating deterioration, and assessing new therapies. Current methods of detecting and quantifying DPN, such as neurophysiology, lack sensitivity, require expert assessment and focus primarily on large nerve fibers. However, the earliest damage to nerve fibers in diabetic neuropathy is to the small nerve fibers. At present, small nerve fiber damage is currently assessed using skin/nerve biopsy; both are invasive technique and are not suitable for repeated investigations.

The visibility of disaster deaths in news images : a comparison of newspapers from 15 countries

The extent to which newspapers display graphic images of death has rarely been studied in relation to the degree of the visibility of bodies, nor do many comparative analyses exist. This has led to a narrow understanding of how and why audiences are exposed to human suffering around the world. In examining newspaper images of the dead from the 2010 Haiti earthquake across 15 countries, this study develops a graphic image content scale to measure such visualizations. It finds significant differences in graphic images across the studied sample, both in terms of the amount of images and the degree of visibility of death. The study argues that major sociocultural influences, such as different religious traditions and societal levels of violence are part of the reason for the differences.

Knitted images : corporeal theorising

Feeling the wool and needles and constructing the knitting is very different to looking at knitting or thinking about knitting. Creating with the material slows everything down enough to enable significant connection with the process. Knitting as a mode for researching involves corporeal activity/philosophy that foregrounds a physical rationality, and this offers critical investigation of knowledge conventions that hierarchize intellectual activity as something that seeks to justify or clarify via a cerebral mode of presenting reasonable and rational arguments...

Knitted images : specimens

Knitting, as a conduit for multiple literacies takes on embodied practice and becomes research, investigation, theorization, and brings about physical and metaphysical theorizing on Deleuzian and Guattarian (1980/1987) concepts of the rhizome: the looping and constructing of the knitted planes prompt thoughts about the project that seem just ‘beyond the level of consciousness’ (Semetsky 2007, p. 200)...

Knitted images : interception

Feeling the wool and needles and constructing the knitting is very different to looking at knitting or thinking about knitting. Creating with the material slows everything down enough to enable significant connection with the process. Knitting as a mode for researching involves corporeal activity/philosophy that foregrounds a physical rationality, and this offers critical investigation of knowledge conventions that hierarchize intellectual activity as something that seeks to justify or clarify via a cerebral mode of presenting reasonable and rational arguments...

Knitted images : drawing patterns

This Arts Based Education Research (Eisner 2008) work provides potent opportunity to consider different problems and challenges that impact on the progress of research (art as data making) and the theories being explored. It provides opportunity to transport ideas across between research activity, and teaching practices...

Knitted images : cartographies

Through the making of these works I research teachers. Here, I push a/r/tography (Irwin & Springgay 2008) into ca/r/tography - a process of mapping that is multitexural, mutable; moving between theorization, creation, process, research, and mapped by me as I wander between artist, researcher, teacher...

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Images inside a reflecting spherical surface with possible application in astronomy

This report studies an algebraic equation whose solution gives the image system of a source of light as seen by an observer inside a reflecting spherical surface. The equation is looked at numerically using GeoGebra. Under the hypothesis that our galaxy is enveloped by a reflecting interface this becomes a possible model for many mysterious extra galactic observations.

A pilot study on affective classification of facial images for emerging news topics

The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from real-world resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.