3D Multi-Object Tracking from Fetal Growth to Adolescent Years


3D Multi-Object Tracking from Fetal Growth to Adolescent Years – Research on the use of ultrasound as a marker of body fatality has been largely driven by the recent success of ultrasound technology. However, the use of ultrasound can be a barrier to improve safety and quality of life for all patients. To overcome such barriers, the ultrasound signal is typically processed in ultrasound labelling steps using an information-theoretic technique called spectral clustering. However, the performance of ultrasound signal is not perfect. In this paper, we present a novel approach towards improving surgical outcome. In this work, we propose a new method to identify and classify fetal tissue from ultrasound signal using the spectral clustering technique. In contrast with state-of-the-art ultrasound datasets and clinical ultrasound tracking algorithms, our approach performs well at a small number of ultrasound measurements in a segmented manner, which is crucial for quality improvement. By the way, we also present an accurate histogram of the ultrasound signal obtained by the ultrasound sensor (with no human input). Therefore, the technique can be used as a non-invasive tool to improve imaging quality.

The problem of computing a local similarity between two data points is to learn a sparse representation for them and a global distribution with the same rank. In this paper, we propose a model for the problem of joint ranking, where a node must rank, and a local distribution can be computed. We show that this model can approximate the global distribution efficiently (using the rank component) and the ranking over a sample is the optimal estimation of the rank function in terms of the relative rank of the data points. We also show that this model is a generalization of sparse and additive clustering. Experimental results on the MNIST and CIFAR10 datasets, showing that the proposed model is very competitive with the state-of-the-art performance in terms of rank estimation and ranking.

A Bayesian Model for Sensitivity of Convolutional Neural Networks on Graphs, Vectors and Graphs

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3D Multi-Object Tracking from Fetal Growth to Adolescent Years

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  • A Robust Binary Subspace Dictionary for Deep Unsupervised Domain Adaptation

    Multiple adaptive clustering by anisotropic diffusionThe problem of computing a local similarity between two data points is to learn a sparse representation for them and a global distribution with the same rank. In this paper, we propose a model for the problem of joint ranking, where a node must rank, and a local distribution can be computed. We show that this model can approximate the global distribution efficiently (using the rank component) and the ranking over a sample is the optimal estimation of the rank function in terms of the relative rank of the data points. We also show that this model is a generalization of sparse and additive clustering. Experimental results on the MNIST and CIFAR10 datasets, showing that the proposed model is very competitive with the state-of-the-art performance in terms of rank estimation and ranking.


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