Temporally Consistent Horizon Lines

The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision. For instance, in navigation of autonomous vehicles or driver assistance, it can be used to improve 3D reconstruction as well as for semantic interpretation of dynamic environments. While both algorithms and datasets exist for single images, the problem of horizon line estimation from video sequences has not gained attention. In this paper, we show how convolutional neural networks are able to utilise the temporal consistency imposed by video sequences in order to increase the accuracy and reduce the variance of horizon line estimates. A novel CNN architecture with an improved residual convolutional LSTM is presented for temporally consistent horizon line estimation. We propose an adaptive loss function that ensures stable training as well as accurate results. Furthermore, we introduce an extension of the KITTI dataset which contains precise horizon line labels for 43699 images across 72 video sequences. A comprehensive evaluation shows that the proposed approach consistently achieves superior performance compared with existing methods

Robust Shape Fitting for 3D Scene Abstraction

Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such as cuboids or cylinders. Inferring these primitives is important for attaining high-level, abstract scene descriptions. Previous approaches for primitive-based abstraction estimate shape parameters directly and are only able to reproduce simple objects. In contrast, we propose a robust estimator for primitive fitting, which meaningfully abstracts complex real-world environments using cuboids. A RANSAC estimator guided by a neural network fits these primitives to a depth map. We condition the network on previously detected parts of the scene, parsing it one-by-one. To obtain cuboids from single RGB images, we additionally optimise a depth estimation CNN end-to-end. Naively minimising point-to-primitive distances leads to large or spurious cuboids occluding parts of the scene. We thus propose an improved occlusion-aware distance metric correctly handling opaque scenes. Furthermore, we present a neural network based cuboid solver which provides more parsimonious scene abstractions while also reducing inference time. The proposed algorithm does not require labour-intensive labels, such as cuboid annotations, for training. Results on the NYU Depth v2 dataset demonstrate that the proposed algorithm successfully abstracts cluttered real-world 3D scene layouts

Pilot Study on Improving Crash Data Accuracy in Kentucky through University Collaboration

Without high-quality crash data and robust interpretive/analytical tools to analyze these data, transportation agencies will struggle to develop evidence-based strategies for improving road safety. Crash narratives are one element of crash reports that pose especially acute interpretive challenges. These narratives supplement coded data and give an account of incidents authored by responding law enforcement officers. Despite their value, conducting manual reviews of the 150,000+ crash reports and narratives issued in Kentucky each year is not feasible. To address this challenge, reviewers examined approximately 8,000 crash narratives from calendar year 2020 using a proprietary web-based quality control tool to identify discrepancies between narratives and coded data. The most pronounced inconsistencies between coded data and narratives were found in questions related to aggressive driving, distracted driving, intersection and secondary crashes, and travel direction. Building on this exercise, researchers developed a machine learning algorithm that automatically classifies attributes in crash records based on the interpretation of unstructured narrative text. Although this model performed well, goodness-of-fit metrics showed that a Google AI Language model (Bidirectional Encoder Representations from Transformers [BERT]) was more accurate and precise as well as having better recall. Future crash data quality control efforts that incorporate machine learning applications should use BERT, however, the latest advances in AI technology need to be integrated into new applications and models as they are developed

CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus

We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised as well as self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators as well as to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.Comment: CVPR 202

Hematologic and Chemical Changes Observed during and after Cardiac Arrest in a Canine Model—A Pilot Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90335/1/phco.21.15.1187.33899.pd

Hepatic resection for hepatocellular carcinoma in patients with Child–Pugh's A cirrhosis: is clinical evidence of portal hypertension a contraindication?

AbstractBackgroundAccording to international guidelines [European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD)], portal hypertension (PHTN) is considered a contraindication for liver resection for hepatocellular carcinoma (HCC), and patients should be referred for other treatments. However, this statement remains controversial. The aim of this study was to elucidate surgical outcomes of minor hepatectomies in patients with PHTN (defined by the presence of esophageal varices or a platelet count of <100 000 in association with splenomegaly) and well‐compensated liver disease.MethodsBetween 1997 and 2012, a total of 223 cirrhotic patients [stage A according to the Barcelona Clinic Liver Cancer (BCLC) classification] were eligible for this analysis and were divided into two groups according to the presence (n = 63) or absence (n = 160) of PHTN. The demographic data were comparable in the two patient groups.ResultsOperative mortality was not different (only one patient died in the PHTN group). However, patients with PHTN had higher liver‐related morbidity (29% versus 14%; P = 0.009), without differences in hospital stay (8.8 versus 9.8 days, respectively). The PHTN group showed a worse survival rate only if biochemical signs of liver decompensation existed. Multivariate analysis identified albumin levels as an independent predictive factor for survival.ConclusionsPHTN should not be considered an absolute contraindication to a hepatectomy in cirrhotic patients. Patients with PHTN have short‐ and long‐term results similar to patients with normal portal pressure. A limited hepatic resection for early‐stage tumours is an option for Child–Pugh class A5 patients with PHTN

Preliminary Neurophysiological Evidence of Altered Cortical Activity and Connectivity With Neurologic Music Therapy in Parkinson's Disease

Neurologic Music Therapy (NMT) is a novel impairment-focused behavioral intervention system whose techniques are based on the clinical neuroscience of music perception, cognition, and production. Auditory Stimulation (RAS) is one of the NMT techniques, which aims to develop and maintain a physiological rhythmic motor activity through rhythmic auditory cues. In a series of breakthrough studies beginning in the mid-nineties, we discovered that RAS durably improves gait velocity, stride length, and cadence in Parkinson's disease (PD). No study to date reports the neurophysiological evidence of auditory-motor frequency entrainment after a NMT intervention in the Parkinson's community. We hypothesized that NMT-related motor improvements in PD are due to entrainment-related coupling between auditory and motor activity resulting from an increased functional communication between the auditory and the motor cortices. Spectral analysis in the primary motor and auditory cortices during a cued finger tapping task showed a simultaneous increase in evoked power in the beta-range along with an increased functional connectivity after a course of NMT in a small sample of three older adults with PD. This case study provides preliminary evidence that NMT-based motor rehabilitation may enhance cortical activation in the auditory and motor areas in a synergic manner. With a lack of both control subjects and control conditions, this neuroimaging case-proof of concept series of visible changes suggests potential mechanisms and offers further education on the clinical applications of musical interventions for motor impairments

Comparison of the diagnostic accuracy of three current guidelines for the evaluation of asymptomatic pancreatic cystic neoplasms.

Asymptomatic pancreatic cysts are a common clinical problem but only a minority of these cases progress to cancer. Our aim was to compare the accuracy to detect malignancy of the 2015 American Gastroenterological Association (AGA), the 2012 International Consensus/Fukuoka (Fukuoka guidelines [FG]), and the 2010 American College of Radiology (ACR) guidelines.We conducted a retrospective study at 3 referral centers for all patients who underwent resection for an asymptomatic pancreatic cyst between January 2008 and December 2013. We compared the accuracy of 3 guidelines in predicting high-grade dysplasia (HGD) or cancer in resected cysts. We performed logistic regression analyses to examine the association between cyst features and risk of HGD or cancer.A total of 269 patients met inclusion criteria. A total of 228 (84.8%) had a benign diagnosis or low-grade dysplasia on surgical pathology, and 41 patients (15.2%) had either HGD (n = 14) or invasive cancer (n = 27). Of the 41 patients with HGD or cancer on resection, only 3 patients would have met the AGA guideline\u27s indications for resection based on the preoperative cyst characteristics, whereas 30/41 patients would have met the FG criteria for resection and 22/41 patients met the ACR criteria. The sensitivity, specificity, positive predictive value, negative predictive value of HGD, and/or cancer of the AGA guidelines were 7.3%, 88.2%, 10%, and 84.1%, compared to 73.2%, 45.6%, 19.5%, and 90.4% for the FG and 53.7%, 61%, 19.8%, and 88% for the ACR guidelines. In multivariable analysis, cyst size \u3e3 cm, compared to ≤3 cm, (odds ratio [OR] = 2.08, 95% confidence interval [CI] = 1.11, 4.2) and each year increase in age (OR = 1.07, 95% CI = 1.03, 1.11) were positively associated with risk of HGD or cancer on resection.In patients with asymptomatic branch duct-intraductal papillary mucinous neoplasms or mucinous cystic neoplasms who underwent resection, the prevalence rate of HGD or cancer was 15.2%. Using the 2015 AGA criteria for resection would have missed 92.6% of patients with HGD or cancer. The more inclusive FG and ACR had a higher sensitivity for HGD or cancer but lower specificity. Given the current deficiencies of these guidelines, it will be important to determine the acceptable rate of false-positives in order to prevent a single true-positive