Intel Corporation

Hyundai Motor Company

Kia Corporation

Vishakha Gupta

Luis Carlos Maria Remis

Christina R Strong

Philip R Lantz

Kushal Datta

Arun Raghunath

Steven M Bennett

Hassnaa Moustafa

Meghashree Dattatri Kedalagudde

Scott H Robinson

Alain Kagi

Cagri Tanriover

Subramanya R Dulloor

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a plurality of images captured by a camera. The processor: accesses visual data associated with an image captured by the camera; determines a tile size parameter for partitioning the visual data into a plurality of tiles; partitions the visual data into the plurality of tiles based on the tile size parameter, wherein the plurality of tiles corresponds to a plurality of regions within the image; compresses the plurality of tiles into a plurality of compressed tiles, wherein each tile is compressed independently; generates a tile-based representation of the image, wherein the tile-based representation comprises an array of the plurality of compressed tiles; and stores the tile-based representation of the image on the storage device.

Analytic image format for visual computing

In one embodiment, an apparatus comprises a storage device and a processor. The storage device stores a feature vector index, wherein the feature vector index comprises a sparse-array data structure representing a feature space for a set of labeled feature vectors, wherein the set of labeled feature vectors are assigned to a plurality of classes. The processor is to: receive a query corresponding to a target feature vector; access, via the storage device, a first portion of the feature vector index, wherein the first portion of the feature vector index comprises a subset of labeled feature vectors that correspond to a same portion of the feature space as the target feature vector; determine the corresponding class of the target feature vector based on the subset of labeled feature vectors; and provide a response to the query based on the corresponding class.

Efficient storage and searching of vector datasets

Managing connected data, such as a graph data store, includes a computing device with persistent memory and volatile memory. The computing device stores a graph data store with a plurality of nodes and edges in persistent memory. Each of the edges defines the relationship between at least two of the nodes. The nodes and edges may contain tags and properties containing additional information. In response to a search request query, the computing device generates an iterator object stored in volatile memory with a reference to one or more nodes and/or edges in the graph data store. The split between volatile and persistent memory allocation could be used for other objects, such as allocators and transactions. Other embodiments are described and claimed.

Technologies for managing connected data on persistent memory-based systems

Systems, apparatus, and computer-readable media for managing data storage for vehicle-embedded computer devices (VECDs) are disclosed. Embodiments include a data hierarchy, which classifies data based on the data source, data destination, the intended use of the data or a target application, data processing requirements of the data, and/or delivery time requirements of the data. A VECD may classified obtained data according to the hierarchy and may store the data in different storage devices based on the classification of data. Other embodiments are described and/or claimed.

Technologies for data management in vehicle-based computing platforms

Efficient storage and processing of high-dimensional feature vectors

Embodiments of a system and method for automatic creation of a multimedia presentation or highlight collection from a collection of candidate contents are generally described herein. In some embodiments, each one of a plurality of videos or images in the candidate contents are automatically evaluated for quality, content, metadata, and desirability based on user specified inclusion factors. Inclusion factors may be utilized to generate one or more scores for the candidate contents, which provide for automatic ranking of the candidate contents. Based on scores generated from the selected inclusion factor criteria a highlight collection of images is automatically generated. The highlight collection can be included in a multimedia presentation, in the form of a memory book, slideshow, or digital narrative, and can be automatically generated from the plurality of videos or images.

Automated memory book creation

Growing community of inventors

Seattle, WA, United States of America

Vishakha Gupta