Deep learning for 3D ear detection: A complete pipeline from data generation to segmentation

The human ear has distinguishing features that can be used for identification. Automated ear detection from 3D profile face images plays a vital role in ear-based human recognition. This work proposes a complete pipeline including synthetic data generation and ground-truth data labeling for ear detection in 3D point clouds. The ear detection problem is formulated as a semantic part segmentation problem that detects the ear directly in 3D point clouds of profile face data. We introduce EarNet, a modified version of the PointNet++ architecture, and apply rotation augmentation to handle different pose variations in the real data. We demonstrate that PointNet and PointNet++ cannot manage the rotation of a given object without such augmentation. The synthetic 3D profile face data is generated using statistical shape models. In addition, an automatic tool has been developed and is made publicly available to create ground-truth labels of any 3D public data set that includes co-registered 2D images. The experimental results on the real data demonstrate higher localization as compared to existing state-of-the-art approaches

The k-means algorithm: A comprehensive survey and performance evaluation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions

High latency unmanned ground vehicle teleoperation enhancement by presentation of estimated future through video transformation

Long-distance, high latency teleoperation tasks are difficult, highly stressful for teleoperators, and prone to over-corrections, which can lead to loss of control. At higher latencies, or when teleoperating at higher vehicle speed, the situation becomes progressively worse. To explore potential solutions, this research work investigates two 2D visual feedback-based assistive interfaces (sliding-only and sliding-and-zooming windows) that apply simple but effective video transformations to enhance teleoperation. A teleoperation simulator that can replicate teleoperation scenarios affected by high and adjustable latency has been developed to explore the effectiveness of the proposed assistive interfaces. Three image comparison metrics have been used to fine-tune and optimise the proposed interfaces. An operator survey was conducted to evaluate and compare performance with and without the assistance. The survey has shown that a 900ms latency increases task completion time by up to 205% for an on-road and 147 % for an off-road driving track. Further, the overcorrection-induced oscillations increase by up to 718 % with this level of latency. The survey has shown the sliding-only video transformation reduces the task completion time by up to 25.53 %, and the sliding-and-zooming transformation reduces the task completion time by up to 21.82 %. The sliding-only interface reduces the oscillation count by up to 66.28 %, and the sliding-and-zooming interface reduces it by up to 75.58 %. The qualitative feedback from the participants also shows that both types of assistive interfaces offer better visual situational awareness, comfort, and controllability, and significantly reduce the impact of latency and intermittency on the teleoperation task

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Airway morphology and its influence on OSA severity and surgical intervention: A retrospective study

Introduction: The aim was to assess the relationship between airway morphology and surgical intervention in a cohort of patients presenting with increased body mass index (BMI) and a confirmed diagnosis of obstructive sleep apnoea (OSA). A secondary aim was to revisit the relationship between morphology and OSA severity. Methods: A retrospective analysis was conducted of pre-operative maxillofacial 3D-CT scans of thirty-two patients with a confirmed diagnosis of OSA who received treatment from an ear nose and throat specialist (ENT). Lateral cephalograms were imported into Quick Ceph Studio (Quick Ceph Systems Inc, San Diego, CA, USA) after which linear and angular measurements of selected hard and soft tissues were obtained. 3D-CT images were loaded into the software program 3dMDVultus (3dMD) which permitted 3D visualisation of the airway. Measurements were repeated 3 times on the images of six patients after an interval of two weeks to establish the intraclass correlation coefficient (ICC) for intra-examiner accuracy and reliability. Logistic regression was applied to determine the relationships between morphology, OSA and surgical treatments. Results: A positive correlation was found between age and the apnoea-hypopnea index (AHI). Morphological measurements of the airway did not exhibit a positive relationship with OSA severity. Posterior airway space at the level of the uvula and tongue, the length of the soft palate and position of the hyoid bone were significantly associated with BMI. No variables were found to be correlated with uvulopalatopharyngoplasty (UPPP) surgery. Notwithstanding, airway length and posterior airway space at the level of the uvula tip were significantly associated with tongue channelling. Conclusions: Radiographic airway assessment is an invaluable and opportunistic tool for screening OSA but requires judicial use in its prescription and interpretation. There is little correlation between OSA severity and airway morphology and between surgical intervention and morphology. Additional factors need to be considered before a treatment modality is considered and is best managed in a multidisciplinary setting

A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications