Abstract: A multisensor processing platform includes, in at least some embodiments, a face detector and embedding network for analyzing unstructured data to detect, identify and track any combination of objects (including people) or activities through computer vision algorithms and machine learning. In some embodiments, the unstructured data is compressed by identifying the appearance of an object across a series of frames of the data, aggregating those appearances and effectively summarizing those appearances of the object by a single representative image displayed to a user for each set of aggregated appearances to enable the user to assess the summarized data substantially at a glance. The data can be filtered into tracklets, groups and clusters, based on system confidence in the identification of the object or activity, to provide multiple levels of granularity.

Abstract: Architecture that summarizes a large amount (e.g., thousands of miles) of street-level image/video data of different perspectives and types (e.g., continuous scan-type data and panorama-type data) into a single view that resembles aerial imagery. Polygons surfaces are generated from the scan patterns and the image data is projected onto the surfaces, and then rendered into the desired orthographic projection. The street-level data is processed using a distributed computing approach across cluster nodes. The collection is processed into image tiles on the separate cluster nodes representing an orthographic map projection that can be viewed at various levels of detail. Map features such as lower-level roads, that are at lower elevations than higher-level roads, and are hidden by higher-level overpassing roads, can be navigated in the map. With the summarized data, the maps can be navigated and zoomed efficiently.

Abstract: Architecture that summarizes a large amount (e.g., thousands of miles) of street-level image/video data of different perspectives and types (e.g., continuous scan-type data and panorama-type data) into a single view that resembles aerial imagery. Polygons surfaces are generated from the scan patterns and the image data is projected onto the surfaces, and then rendered into the desired orthographic projection. The street-level data is processed using a distributed computing approach across cluster nodes. The collection is processed into image tiles on the separate cluster nodes representing an orthographic map projection that can be viewed at various levels of detail. Map features such as lower-level roads, that are at lower elevations than higher-level roads, and are hidden by higher-level overpassing roads, can be navigated in the map. With the summarized data, the maps can be navigated and zoomed efficiently.

Abstract: Architecture that summarizes a large amount (e.g., thousands of miles) of street-level image/video data of different perspectives and types (e.g., continuous scan-type data and panorama-type data) into a single view that resembles aerial imagery. Polygons surfaces are generated from the scan patterns and the image data is projected onto the surfaces, and then rendered into the desired orthographic projection. The street-level data is processed using a distributed computing approach across cluster nodes. The collection is processed into image tiles on the separate cluster nodes representing an orthographic map projection that can be viewed at various levels of detail. Map features such as lower-level roads, that are at lower elevations than higher-level roads, and are hidden by higher-level overpassing roads, can be navigated in the map. With the summarized data, the maps can be navigated and zoomed efficiently.

Abstract: An image processing is applied in common to image data for a first image and to image data for a second image. The image data for the first image and the image data for the second image are moreover compared as a basis for matching a visual appearance of images for a stereoscopic presentation. The visual appearance can be matched in the current pair of images by adjusting at least one of the images accordingly and/or in future pairs of images by adjusting parameters of at least one of the image capturing components providing the image data.

Abstract: An apparatus-based method is disclosed for calibration of a 3D field sensor. The method includes accessing a plurality of samples, where the samples are from the 3D field sensor. Each sample represents a magnitude and an orientation of a three-dimensional field sensed by the 3D field sensor. Using the plurality of samples, a plurality of parameters is determined of an ellipsoid. The determination of the plurality of parameters is performed so that the ellipsoid fits the plurality of samples. A transformation is determined that transforms the ellipsoid into a sphere. The transformation is applied to a sample to create a transformed sample. Apparatus and signal bearing media are also disclosed.