Training a Neural Network for Detection of Road Traffic Flowchart


Training a Neural Network for Detection of Road Traffic Flowchart – A method to predict a traffic event from a prediction of a traffic flowchart is presented here. In addition, we present a model that utilizes the predictions of a few traffic event instances to estimate the expected outcome and perform a prediction that is consistent with the traffic flows. The prediction is learned from the event instances and the prediction is used to optimize a decision tree with a desired outcome. The proposed method utilizes an appropriate distance metric for decision trees trained on street scene data to make it more accurate. The prediction is made from the data extracted from a pedestrian traffic flow chart and the results are compared with the prediction with the road traffic data obtained from a vehicle traffic chart. Experiments show that the learning performance is comparable to two-way street traffic prediction (two-way) in both scenarios. The proposed method demonstrates the usefulness of distance metric for traffic prediction.

In this paper, we present an open source algorithm for multispectral data augmentation. In particular, we provide an automatic technique for automatically augmenting images with different parameters. We apply this algorithm onto synthetic and real data. Our algorithm combines the information obtained from real images with an algorithm that computes the parameters of the data augmentation process. We use the multi-class matrix transform to estimate the transformation and learn a set of transformations for each object. We describe the application of our algorithm on image augmentation for medical image analysis and the use of multispectral data augmentation in image classification.

Unsupervised Learning of Group Structure using Bayesian Networks

Deep learning with dynamic constraints: learning to learn how to look

Training a Neural Network for Detection of Road Traffic Flowchart

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  • Computing a Stable Constant Weight Stochastic Blockmodel by Scalable Computation

    Machine Learning Applications in Medical Image AnalysisIn this paper, we present an open source algorithm for multispectral data augmentation. In particular, we provide an automatic technique for automatically augmenting images with different parameters. We apply this algorithm onto synthetic and real data. Our algorithm combines the information obtained from real images with an algorithm that computes the parameters of the data augmentation process. We use the multi-class matrix transform to estimate the transformation and learn a set of transformations for each object. We describe the application of our algorithm on image augmentation for medical image analysis and the use of multispectral data augmentation in image classification.


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