Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/

Resistance to autosomal dominant Alzheimer's disease in an APOE3 Christchurch homozygote: a case report.

We identified a PSEN1 (presenilin 1) mutation carrier from the world's largest autosomal dominant Alzheimer's disease kindred, who did not develop mild cognitive impairment until her seventies, three decades after the expected age of clinical onset. The individual had two copies of the APOE3 Christchurch (R136S) mutation, unusually high brain amyloid levels and limited tau and neurodegenerative measurements. Our findings have implications for the role of APOE in the pathogenesis, treatment and prevention of Alzheimer's disease

Supervised machine learning based multi-task artificial intelligence classification of retinopathies

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en

Automated hippocampal segmentation in patients with epilepsy: Available free online

Hippocampal sclerosis, a common cause of refractory focal epilepsy, requires hippocampal volumetry for accurate diagnosis and surgical planning. Manual segmentation is time-consuming and subject to interrater/intrarater variability. Automated algorithms perform poorly in patients with temporal lobe epilepsy. We validate and make freely available online a novel automated method

On the taxonomic resolution of pollen and spore records of Earth’s vegetation

Premise of research. Pollen and spores (sporomorphs) are a valuable record of plant life and have provided information on subjects ranging from the nature and timing of evolutionary events to the relationship between vegetation and climate. However, sporomorphs can be morphologically similar at the species, genus, or family level. Studies of extinct plant groups in pre-Quaternary time often include dispersed sporomorph taxa whose parent plant is known only to the class level. Consequently, sporomorph records of vegetation suffer from limited taxonomic resolution and typically record information about plant life at a taxonomic rank above species.Methodology. In this article, we review the causes of low taxonomic resolution, highlight examples where this has hampered the study of vegetation, and discuss the strategies researchers have developed to overcome the low taxonomic resolution of the sporomorph record. Based on this review, we offer our views on how greater taxonomic precision might be attained in future work. Pivotal results. Low taxonomic resolution results from a combination of several factors, including inadequate reference collections, the absence of sporomorphs in situ in fossilized reproductive structures, and damage following fossilization. A primary cause is the difficulty of accurately describing the very small morphological differences between species using descriptive terminology, which results in palynologists classifying sporomorphs conservatively at the genus or family level to ensure that classifications are reproducible between samples and between researchers. Conclusions. In our view, the most promising approach to the problem of low taxonomic resolution is a combination of high-resolution imaging and computational image analysis. In particular, we encourage palynologists to explore the utility of microscopy techniques that aim to recover morphological information from below the diffraction limit of light and to employ computational image analyses to consistently quantify small morphological differences between species

The virtual human face – superimposing the simultaneously captured 3D photorealistic skin surface of the face on the untextured skin image of the CBCT Scan

The aim of this study was to evaluate the impact of simultaneous capture of the three-dimensional (3D) surface of the face and cone beam computed tomography (CBCT) scan of the skull on the accuracy of their registration and superimposition. 3D facial images were acquired in 14 patients using the Di3d (Dimensional Imaging, UK) imaging system and i-CAT CBCT scanner. One stereophotogrammetry image was captured at the same time as the CBCT and another one hour later. The two stereophotographs were then individually superimposed over the CBCT using VRmesh. Seven patches were isolated on the final merged surfaces. For the whole face and each individual patch; maximum and minimum range of deviation between surfaces, absolute average distance between surfaces, and standard deviation for the 90th percentile of the distance errors were calculated. The superimposition errors of the whole face for both captures revealed statistically significant differences (P=0.00081). The absolute average distances in both separate and simultaneous captures were 0.47mm and 0.27mm, respectively. The level of superimposition accuracy in patches from separate captures ranged between 0.3 and 0.9mm, while that of simultaneous captures was 0.4mm. Simultaneous capture of Di3d and CBCT images significantly improved the accuracy of superimposition of these image modalities