Abstract: A data management system includes a data reconciliation engine that identifies data sources that contain data records referencing a resource and determines whether each of the identified data sources is a creative data source or an additive data source. When all of the identified data sources are additive data sources, the reconciliation engine terminates a data reconciliation process. When all of the identified data sources are not additive data sources, the reconciliation engine finds a first creative data source from among the identified data sources, and initiates the data reconciliation process by merging data from the identified data sources including the first creative data source, one data source-by-one data source, into a reconciled data record.

Abstract: A method for use in or for detecting a downhole feature in a well comprises transmitting an electromagnetic signal from a first position located substantially at or adjacent to surface through a first space to the downhole feature. The method further comprises receiving an electromagnetic signal at a second position located substantially at or adjacent to surface after reflection of the transmitted electromagnetic signal from the downhole feature and after propagation of the reflected electromagnetic signal through a second space. The method may comprise sealing the well before transmitting the electromagnetic signal. Such a method may be used to detector determine a distance from surface to a downhole feature such as a fluid interface in a completed production, injection or observation well.

Abstract: A data management system includes a data reconciliation engine that identifies data sources that contain data records referencing a resource and determines whether each of the identified data sources is a creative data source or an additive data source. When all of the identified data sources are additive data sources, the reconciliation engine terminates a data reconciliation process. When all of the identified data sources are not additive data sources, the reconciliation engine finds a first creative data source from among the identified data sources, and initiates the data reconciliation process by merging data from the identified data sources including the first creative data source, one data source-by-one data source, into a reconciled data record.

Abstract: In one general aspect, a computer system can include instructions configured to store on a non-transitory computer-readable storage medium. The computer system can include a subgraph transformer configured to transform a plurality of subgraphs of a source graph into a plurality of transformed subgraphs, and configured to define a target graph that is a transformed version of the source graph based on the plurality of transformed subgraphs. The computer system can include a change detector configured to receive an indicator that a portion of the source graph has been changed, and a synchronization module configured to synchronize a portion of the target graph with the changed portion of the source graph.

Abstract: A method and system for forming a holographic structure in a material. The holographic structure is configured to project a selected target image in the far field under illumination of the holographic structure by a laser. The method calculates a modified design for the holographic structure that encodes a unique identifier within the holographic structure for projecting the target image. The method modifies the material by mapping features corresponding to the modified design into the material so as to form the holographic structure. A basic check of the authenticity of the material is performed by checking whether a projected replica of the target image is as expected. A more detailed check of the authenticity of the material is performed by directly inspecting the features in the holographic structure.

Abstract: Region of interest detection in raw time of flight images is described. For example, a computing device receives at least one raw image captured for a single frame by a time of flight camera. The raw image depicts one or more objects in an environment of the time of flight camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.

Abstract: A method of forming an optical device comprises applying a laser beam to a target area of the surface so as to selectively heat material of the surface thereby to provide transfer of material due to a surface tension gradient, wherein the surface is such that, when liquid, parts of the surface at higher temperatures have a higher surface tension than adjacent parts of the surface at lower temperatures.

Abstract: Images of foreground objects in a scene are generated by causing electromagnetic radiation to be emitted having a first spectral power distribution from a surface of a first foreground object, which is adjacent or at least partially obscured by a second foreground object. A first image of both of the first and second foreground objects is acquired while the first foreground object emits electromagnetic radiation with the first spectral power distribution. A second image of the first and second foreground objects is acquired while the first foreground object is not emitting electromagnetic radiation or is emitting electromagnetic radiation with a second spectral power distribution which is different to the first spectral power distribution. An alpha matte of the first and second foreground objects is generated based on a comparison of the first image and second image.

Abstract: Tracking hand or body pose from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a prediction engine takes a single frame of image data and predicts a distribution over a pose of a hand or body depicted in the image data. In examples, a stochastic optimizer has a pool of candidate poses of the hand or body which it iteratively refines, and samples from the predicted distribution are used to replace some candidate poses in the pool. In some examples a best candidate pose from the pool is selected as the current tracked pose and the selection processes uses a 3D model of the hand or body.

Abstract: Region of interest detection in raw time of flight images is described. For example, a computing device receives at least one raw image captured for a single frame by a time of flight camera. The raw image depicts one or more objects in an environment of the time of flight camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.

Abstract: Region of interest detection in raw time of flight images is described. For example, a computing device receives at least one raw image captured for a single frame by a time of flight camera. The raw image depicts one or more objects in an environment of the time of flight camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.

Abstract: Image registration is described. In an embodiment an image registration system executes automatic registration of images, for example medical images. In an example, semantic information is computed for each of the images to be registered comprising information about the types of objects in the images and the certainty of that information. In an example a mapping is found to register the images which takes into account the intensities of the image elements as well as the semantic information in a manner which is weighted by the certainty of that semantic information. For example, the semantic information is computed by estimating posterior distributions for the locations of anatomical structures by using a regression forest and transforming the posterior distributions into a probability map. In an example the mapping is found as a global point of inflection of an energy function, the energy function having a term related to the semantic information.

Abstract: Images of foreground objects in a scene are generated by causing electromagnetic radiation to be emitted having a first spectral power distribution from a surface of a first foreground object, which is adjacent or at least partially obscured by a second foreground object. A first image of both of the first and second foreground objects is acquired whilst the first foreground object emits electromagnetic radiation with the first spectral power distribution. A second image of the first and second foreground objects is acquired whilst the first foreground object is not emitting electromagnetic radiation or is emitting electromagnetic radiation with a second spectral power distribution which is different to the first spectral power distribution. An alpha matte of the first and second foreground objects is generated based on a comparison of the first image and second image.

Abstract: Tracking hand or body pose from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a prediction engine takes a single frame of image data and predicts a distribution over a pose of a hand or body depicted in the image data. In examples, a stochastic optimizer has a pool of candidate poses of the hand or body which it iteratively refines, and samples from the predicted distribution are used to replace some candidate poses in the pool. In some examples a best candidate pose from the pool is selected as the current tracked pose and the selection processes uses a 3D model of the hand or body.

Abstract: A method of sensing depth using an RGB camera. In an example method, a color image of a scene is received from an RGB camera. The color image is applied to a trained machine learning component which uses features of the image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the RGB camera. In various examples, the machine learning component comprises one or more entangled geodesic random decision forests.

Abstract: Images of foreground objects in a scene are generated by causing electromagnetic radiation to be emitted having a first spectral power distribution from a surface of a first foreground object, which is adjacent or at least partially obscured by a second foreground object. A first image of both of the first and second foreground objects is acquired while the first foreground object emits electromagnetic radiation with the first spectral power distribution. A second image of the first and second foreground objects is acquired while the first foreground object is not emitting electromagnetic radiation or is emitting electromagnetic radiation with a second spectral power distribution which is different to the first spectral power distribution. An alpha matte of the first and second foreground objects is generated based on a comparison of the first image and second image.

Abstract: Systems and methods for motion capture of a subject, suitable for use in an underground mining environment.

Abstract: Tracking hand or body pose from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a prediction engine takes a single frame of image data and predicts a distribution over a pose of a hand or body depicted in the image data. In examples, a stochastic optimizer has a pool of candidate poses of the hand or body which it iteratively refines, and samples from the predicted distribution are used to replace some candidate poses in the pool. In some examples a best candidate pose from the pool is selected as the current tracked pose and the selection processes uses a 3D model of the hand or body.

Abstract: A method of sensing depth using an infrared camera. In an example method, an infrared image of a scene is received from an infrared camera. The infrared image is applied to a trained machine learning component which uses the intensity of image elements to assign all or some of the image elements a depth value which represents the distance between the surface depicted by the image element and the infrared camera. In various examples, the machine line component comprises one or more random decision forests.

Abstract: A method for use in or for detecting a downhole feature in a well comprises transmitting an electromagnetic signal from a first position located substantially at or adjacent to surface through a first space to the downhole feature. The method further comprises receiving an electromagnetic signal at a second position located substantially at or adjacent to surface after reflection of the transmitted electromagnetic signal from the downhole feature and after propagation of the reflected electromagnetic signal through a second space. The method may comprise sealing the well before transmitting the electromagnetic signal. Such a method may be used to detector determine a distance from surface to a downhole feature such as a fluid interface in a completed production, injection or observation well.