The Interactive Biometric Platform

The Interactive Biometric Platform – To obtain an informed opinion on the proposed method, the authors have designed two software projects: The Inter-Agency Biometric Machine Learning, which implements the new algorithms and is based on a prototype. The Machine Learning project focuses on the identification of the object, the data collection and the user experience. The main contributions of the Machine Learning project involved: (1) developing an online algorithm for solving the object recognition problem, (2) building an end-to-end solution for the application, (3) developing the algorithm and making use of the generated samples.

We present a machine learning method for automatically categorizing airbag videos using image segmentation. The method uses a visual representation and a hierarchical clustering to find the category-specific airbag segmentation clusters. We first provide a baseline for such a machine learning algorithm. After that, we provide three preliminary experiments to compare the proposed method with state of the art image segmentation methods. We observe that the proposed method performs significantly faster than state-of-the-art methods. Finally, we compare the proposed method with the state of the art methods in terms of both training and evaluation time on video datasets.

Fast Partition Learning for Partially Observed Graphs

A Comparison of Performance Measures of Logical and Probabilistic Models

The Interactive Biometric Platform

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  • Probabilistic Models for Constraint-Free Semantic Parsing with Sparse Linear Programming

    A Bayesian Multiclass Approach for Estimation of Airbag Trajectories from Mobile Health AppsWe present a machine learning method for automatically categorizing airbag videos using image segmentation. The method uses a visual representation and a hierarchical clustering to find the category-specific airbag segmentation clusters. We first provide a baseline for such a machine learning algorithm. After that, we provide three preliminary experiments to compare the proposed method with state of the art image segmentation methods. We observe that the proposed method performs significantly faster than state-of-the-art methods. Finally, we compare the proposed method with the state of the art methods in terms of both training and evaluation time on video datasets.


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