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Full-Text Articles in Physical Sciences and Mathematics
Countnet3d: A 3d Computer Vision Approach To Infer Counts Of Occluded Objects With Quantified Uncertainty, Stephen W. Nelson
Countnet3d: A 3d Computer Vision Approach To Infer Counts Of Occluded Objects With Quantified Uncertainty, Stephen W. Nelson
Theses and Dissertations
3D scene understanding is an important problem that has experienced great progress in recent years, in large part due to the development of state-of-the-art methods for 3D object detection. However, the performance of 3D object detectors can suffer in scenarios where extreme occlusion of objects is present, or the number of object classes is large. In this paper, we study the problem of inferring 3D counts from densely packed scenes with heterogeneous objects. This problem has applications to important tasks such as inventory management or automatic crop yield estimation. We propose a novel regression-based method, CountNet3D, that uses mature 2D …
Sequential Survival Analysis With Deep Learning, Seth William Glazier
Sequential Survival Analysis With Deep Learning, Seth William Glazier
Theses and Dissertations
Survival Analysis is the collection of statistical techniques used to model the time of occurrence, i.e. survival time, of an event of interest such as death, marriage, the lifespan of a consumer product or the onset of a disease. Traditional survival analysis methods rely on assumptions that make it difficult, if not impossible to learn complex non-linear relationships between the covariates and survival time that is inherent in many real world applications. We first demonstrate that a recurrent neural network (RNN) is better suited to model problems with non-linear dependencies in synthetic time-dependent and non-time-dependent experiments.