Abstract: Technologies are described for retrieving documents using image representations in the documents and is based on intra-image features. The identification of elements within an image representation can allow for deeper understanding of the image representation and for better relating image representations based on their intra-image features. The intra-image features present in image representations can be used in searches. Search results can further be reranked to improve search results. For example, reranking can allow search results to conform to intra-image dominant image features.

Abstract: Technologies are described for retrieving documents using image representations in the documents and is based on intra-image features. The identification of elements within an image representation can allow for deeper understanding of the image representation and for better relating image representations based on their intra-image features. The intra-image features present in image representations can be used in searches. Search results can further be reranked to improve search results. For example, reranking can allow search results to conform to intra-image dominant image features.

Abstract: Technologies are described for reconstructing physical objects which are preserved or represented in pictorial records. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing physical models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of the object to be described. The one or more feature vector representations of the object can be used individually or in combination with other descriptors for retrieval and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: Technologies are described for reconstructing facial models which are preserved images or images captured from security cameras. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing facial models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of a face to be described. The one or more feature vector representations of a target face can be used individually or in combination with other descriptors for recognition, retrieval, and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: Technologies are described for reconstructing physical objects which are preserved or represented in pictorial records. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing physical models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of the object to be described. The one or more feature vector representations of the object can be used individually or in combination with other descriptors for retrieval and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: Technologies are described for reconstructing facial models which are preserved images or images captured from security cameras. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing facial models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of a face to be described. The one or more feature vector representations of a target face can be used individually or in combination with other descriptors for recognition, retrieval, and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: Technologies are described for retrieving documents using image representations in the documents and is based on intra-image features. The identification of elements within an image representation can allow for deeper understanding of the image representation and for better relating image representations based on their intra-image features. The intra-image features present in image representations can be used in searches. Search results can further be reranked to improve search results. For example, reranking can allow search results to conform to intra-image dominant image features.

Abstract: Technologies are described for reconstructing physical objects which are preserved or represented in pictorial records. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing physical models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of the object to be described. The one or more feature vector representations of the object can be used individually or in combination with other descriptors for retrieval and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: Technologies are described for reconstructing facial models which are preserved images or images captured from security cameras. The reconstructed models can be three-dimensional (3D) point clouds and can be compared to existing facial models and/or other reconstructed models based on physical geometry. The 3D point cloud models can be encoded into one or more latent space feature vector representations which can allow both local and global geometric properties of a face to be described. The one or more feature vector representations of a target face can be used individually or in combination with other descriptors for recognition, retrieval, and classification tasks. Neural networks can be used in the encoding of the one or more feature vector representations.

Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: The methods, systems, and processes described herein enable one to use 2D images to construct a 3D model, perform a search for similar stored models, and return results based on the similarity of the 3D model to stored models. This is accomplished, for example, by receiving a query of 2D images, generating a 3D model from the 2D images, comparing the 3D model to archived 3D models, ranking the comparisons, and responding to the query based on the ranked results.