Machine Learning Methods for Multi-Step Traffic Acquisition


Machine Learning Methods for Multi-Step Traffic Acquisition – Sparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.

In this paper, the proposed technique for estimating the anatomical position in 3D spaces is considered. The anatomical coordinates are first predicted into a new space using an efficient algorithm where the first prediction is replaced by a new set of coordinates. The second prediction is used to estimate the anatomical position. The estimation is then used to perform a classification of the 2D space and to select the most suitable anatomical location for scanning. The proposed technique is used in the clinical practice in the field of CT image classification in particular. In the first version of the algorithm in our experiments we performed experiments on a 3D space using different scan images. The performance was improved by using our proposed method.

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Machine Learning Methods for Multi-Step Traffic Acquisition

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  • Deep Learning with Risk-Aware Adaptation for Driver Test Count Prediction

    A Novel Model of CT Imaging Based on Statistical Estimation of Surgical TechniqueIn this paper, the proposed technique for estimating the anatomical position in 3D spaces is considered. The anatomical coordinates are first predicted into a new space using an efficient algorithm where the first prediction is replaced by a new set of coordinates. The second prediction is used to estimate the anatomical position. The estimation is then used to perform a classification of the 2D space and to select the most suitable anatomical location for scanning. The proposed technique is used in the clinical practice in the field of CT image classification in particular. In the first version of the algorithm in our experiments we performed experiments on a 3D space using different scan images. The performance was improved by using our proposed method.


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