Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach


Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach – Many computer vision tasks involve segmentation and analysis, both important aspects of the task at hand. We present a novel approach to automatic segmentation of facial features from face images. Our method is simple and fast, and works well when trained in supervised (i.e., on the face image from the training set) or unlabeled (i.e., on the face images from the unlabeled set). To learn a discriminative model for a particular task, we first train a discriminative model for each face image in order to extract a global discriminative representation from the face images. Our system is evaluated on a set of datasets from a large-scale multi-view face recognition system. The results indicate that the discriminative model learned by our method consistently outperforms the unlabeled models with respect to a variety of segmentation and analysis tasks. Our system is able to recognize faces with low or no annotation cost.

We propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.

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Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach

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  • Towards the Application of Machine Learning to Predict Astrocytoma Detection

    A Multi-View Hierarchical Clustering Framework for Optimal Vehicle RoutingWe propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.


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