Abstract: Deep Learning models complex phenomena. Deep Learning is also used in Intelligent Transportation Systems for vehicular perception and traffic analysis. In short-term traffic forecasting, Deep Learning’s ability to produce good results has created a…
Abstract: We detect semantic lines in natural scenes. Many methods treat semantic line detection as a special case of object detection. These methods neglect line characteristics and perform poorly. Lines are simpler geometrically than complex…
Abstract: Driver assistance and intelligent autonomous vehicles will benefit from automatic human activity recognition and prediction. In this article, we present a novel single image driver action recognition algorithm inspired by human perception that selectively…
Abstract: Electronic medical records are used to build adverse event prediction models for resource management. Many machine learning methods struggle with sparse, irregularly-sampled data. Carrying the last value forward or linear regression can interpolate missing…
Abstract: Deep networks estimate people densities in images for modern crowd counting methods. Few video sequences use temporal consistency, and those that do only apply weak smoothness constraints across frames. This paper proposes estimating people…
Abstract: Convolutional neural networks trained end-to-end to regress dense disparities estimate stereo image depth with unmatched accuracy. If large amounts of labelled samples are available for training, this is possible for most tasks. Real-world applications…
Abstract: Semantic segmentation has advanced rapidly with convolutional neural networks (CNNs). Despite their success, deep learning methods require large real-world datasets with pixel-level annotations. Many researchers use synthetic data with free annotations because pixel-level semantic…
Abstract: Quantifying deep neural classifier confidence is difficult but essential for safety-critical applications. This paper introduces the true class probability (TCP) as a model confidence target criterion. TCP outperforms maximum class probability (MCP) for confidence…
Abstract: Autonomous driving requires accurate and fast point cloud 3D object detection. One-stage 3D object detection methods can achieve real-time performance, but they are dominated by anchor-based detectors that are inefficient and require post-processing. This…
Abstract: Biomedical Relation Extraction (RE) systems classify biomedical entities to improve our understanding of biological and medical processes. Most cutting-edge systems use deep learning to find relationships between similar entities like proteins or drugs. Ontologies,…
Abstract: Highway and main road cracks are common. Manual road crack evaluation is tedious, inaccurate, and difficult to implement. The complex ambient conditions—illumination, shadow, dust, and crack shape—make the computer vision-based solution difficult. Most cracks…
Abstract: Background: Pacing leads with miniaturized accelerometers monitor myocardial function. This requires acceleration signal functional indices. A method that automatically detects aortic valve opening (AVO) and closure (AVC) will aid such extraction. We used high-fidelity…
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