Real-time object detection using monocular vision for low-cost automotive sensing systems

This work addresses the problem of real-time object detection in automotive environments using monocular vision. The focus is on real-time feature detection, tracking, depth estimation using monocular vision and finally, object detection by fusing visual saliency and depth information. Firstly, a novel feature detection approach is proposed for extracting stable and dense features even in images with very low signal-to-noise ratio. This methodology is based on image gradients, which are redefined to take account of noise as part of their mathematical model. Each gradient is based on a vector connecting a negative to a positive intensity centroid, where both centroids are symmetric about the centre of the area for which the gradient is calculated. Multiple gradient vectors define a feature with its strength being proportional to the underlying gradient vector magnitude. The evaluation of the Dense Gradient Features (DeGraF) shows superior performance over other contemporary detectors in terms of keypoint density, tracking accuracy, illumination invariance, rotation invariance, noise resistance and detection time. The DeGraF features form the basis for two new approaches that perform dense 3D reconstruction from a single vehicle-mounted camera. The first approach tracks DeGraF features in real-time while performing image stabilisation with minimal computational cost. This means that despite camera vibration the algorithm can accurately predict the real-world coordinates of each image pixel in real-time by comparing each motion-vector to the ego-motion vector of the vehicle. The performance of this approach has been compared to different 3D reconstruction methods in order to determine their accuracy, depth-map density, noise-resistance and computational complexity. The second approach proposes the use of local frequency analysis of i ii gradient features for estimating relative depth. This novel method is based on the fact that DeGraF gradients can accurately measure local image variance with subpixel accuracy. It is shown that the local frequency by which the centroid oscillates around the gradient window centre is proportional to the depth of each gradient centroid in the real world. The lower computational complexity of this methodology comes at the expense of depth map accuracy as the camera velocity increases, but it is at least five times faster than the other evaluated approaches. This work also proposes a novel technique for deriving visual saliency maps by using Division of Gaussians (DIVoG). In this context, saliency maps express the difference of each image pixel is to its surrounding pixels across multiple pyramid levels. This approach is shown to be both fast and accurate when evaluated against other state-of-the-art approaches. Subsequently, the saliency information is combined with depth information to identify salient regions close to the host vehicle. The fused map allows faster detection of high-risk areas where obstacles are likely to exist. As a result, existing object detection algorithms, such as the Histogram of Oriented Gradients (HOG) can execute at least five times faster. In conclusion, through a step-wise approach computationally-expensive algorithms have been optimised or replaced by novel methodologies to produce a fast object detection system that is aligned to the requirements of the automotive domain

Best Practices in Stroke Quality Improvement

https://scholarlycommons.henryford.com/detstrokeconf2019/1006/thumbnail.jp

Dense Gradient-based Features (DeGraF) for Computationally Efficient and Invariant Feature Extraction in Real-time Applications

We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computationally expensive dense scene sensing, we show that Dense Gradient-based Features (DeGraF) can be derived based on initial multi-scale division of Gaussian preprocessing, weighted centroid gradient calculation and either local saliency (DeGraF-α) or signal-to-noise inspired (DeGraF-β) final stage filtering. We present two variants (DeGraF-α / DeGraF-β) of which the signal-to-noise based approach is shown to perform admirably against the state of the art in terms of feature density, computational efficiency and feature stability. Our approach is evaluated under a range of environmental conditions typical of automotive sensing applications with strong feature density requirements

Deep Learning from Label Proportions for Emphysema Quantification

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.Comment: Accepted to MICCAI 201

Impact of the COVID-19 Pandemic on Acute Stroke Care, Time Metrics, Outcomes, and Racial Disparities in a Southeast Michigan Health System

BACKGROUND: COVID-19 has impacted acute stroke care with several reports showing worldwide drops in stroke caseload during the pandemic. We studied the impact of COVID-19 on acute stroke care in our health system serving Southeast Michigan as we rolled out a policy to limit admissions and transfers. METHODS: in this retrospective study conducted at two stroke centers, we included consecutive patients presenting to the ED for whom a stroke alert was activated during the period extending from 3/20/20 to 5/20/20 and a similar period in 2019. We compared demographics, time metrics, and discharge outcomes between the two groups. RESULTS: of 385 patients presented to the ED during the two time periods, 58% were African American. There was a significant decrease in the number of stroke patients presenting to the ED and admitted to the hospital between the two periods (p \u3c0.001). In 2020, patients had higher presenting NIHSS (median: 2 vs 5, p = 0.012), discharge NIHSS (median: 2 vs 3, p = 0.004), and longer times from LKW to ED arrival (4.8 vs 9.4 h, p = 0.031) and stroke team activation (median: 10 vs 15 min, p = 0.006). In 2020, stroke mimics rates were lower among African Americans. There were fewer hospitalizations (p \u3c0.001), and transfers from outside facilities (p = 0.015). CONCLUSION: a trend toward faster stroke care in the ED was observed during the pandemic along with dramatically reduced numbers of ED visits, hospitalizations and stroke mimics. Delayed ED presentations and higher stroke severity characterized the African American population, highlighting deepening of racial disparities during the pandemic

Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice.Comment: First three authors contributed equall