DenseNet: Efficient Segmentation of High-Quality Faces from RGB-D Data


DenseNet: Efficient Segmentation of High-Quality Faces from RGB-D Data – We present a novel approach for extracting the 3D geometry and pose of faces from 3D facial videos. As the dataset is large, we also consider the pose of faces in an environment. We propose two methods to extract 3D geometry and pose of faces from facial videos. One method consists of a pose-aware facial registration, and the other involves an object segmentation. We show that extracting 3D geometry and pose of faces can yield state-of-the-art results in the VOC VOC dataset. Moreover, an initial pose-aware facial registration is proposed, followed by a pose-aware face segmentation. Extensive experimental results on various face datasets demonstrate the effectiveness of our approach.

Deep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.

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DenseNet: Efficient Segmentation of High-Quality Faces from RGB-D Data

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    Deep Learning with Deep Hybrid Feature RepresentationsDeep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.


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