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.

A fundamental issue in all deep vision systems is to classify high-accuracy visual observations. Recent studies have found that deep neural networks outperform the state-of-the-art visual tracking methods in learning from images. This work investigates that deep neural networks also are useful in predicting the state of the visual system and the object category. The main motivation behind this work is to predict the category of the visual system. For this purpose I present a deep-feature learning method based on both feature predictions and a sparse and unsupervised feature representation of the system. Experiments show that the proposed method outperforms state-of-the-art visual tracking and object detection.

The Spatial Proximal Projection for Kernelized Linear Discriminant Analysis

Dependence inference on partial differential equations

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

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  • A Dynamic Bayesian Network Model to Support Fact Checking in Qualitative Fact Checking

    The Importance of Depth for Visual TrackingA fundamental issue in all deep vision systems is to classify high-accuracy visual observations. Recent studies have found that deep neural networks outperform the state-of-the-art visual tracking methods in learning from images. This work investigates that deep neural networks also are useful in predicting the state of the visual system and the object category. The main motivation behind this work is to predict the category of the visual system. For this purpose I present a deep-feature learning method based on both feature predictions and a sparse and unsupervised feature representation of the system. Experiments show that the proposed method outperforms state-of-the-art visual tracking and object detection.


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