Abstract: Systems and methods for large-scale geospatial mosaic generation with image processing in a cloud computing environment. The approaches described herein specifically leverage scalable cloud computing features to facilitate highly parallel, granular image processing. Front-end image processing techniques allow for generation of a user interface that may provide automated material selection with human operator refinement. The user interface may be a web-based design that provides browse version images at lower resolutions to improve performance of the user interface and generating mosaic recipe. In turn, the mosaic recipe may be provided to back-end image processing that coordinates strip-level jobs and tile-level jobs and highly parallel fashion scalable cloud computing nodes. In turn, very large-scale mosaic images may be generated from geospatial images in computationally and cost-effective manner.

Abstract: The present disclosure provides variant OmpG polypeptides, compositions comprising the OmpG variant polypeptides, and methods for using the variant OmpG polypeptides as nanopores for determining the sequence of single stranded nucleic acids. The variant OmpG nanopores reduce the ionic current noise versus the parental OmpG polypeptide from which they are derived and thereby enable sequencing of polynucleotides with single nucleotide resolution. The reduced ionic current noise also provides for the use of these OmpG nanopore variants in other single molecule sensing applications, e.g., protein sequencing.

Abstract: A system for use in a connected kitchen allows a user to find and use traditional recipes as would be found in a recipe book and to have them automatically customized to their language, locality, dietary preferences and the appliances in their kitchen.

Abstract: The present disclosure provides variant OmpG polypeptides, compositions comprising the OmpG variant polypeptides, and methods for using the variant OmpG polypeptides as nanopores for determining the sequence of single stranded nucleic acids. The variant OmpG nanopores reduce the ionic current noise versus the parental OmpG polypeptide from which they are derived and thereby enable sequencing of polynucleotides with single nucleotide resolution. The reduced ionic current noise also provides for the use of these OmpG nanopore variants in other single molecule sensing applications, e.g., protein sequencing.

Abstract: A method of detecting alpha particles may include providing a layer of a scintillating slurry comprising a granular scintillating material in water onto a measurement surface of a sample; positioning the sample having the scintillating slurry within a detection chamber of detection apparatus; detecting photons produced by the granular scintillating material in the scintillating slurry when the granular scintillating material is excited by ionizing alpha radiation emitted by alpha particles within the sample using a photon detector and generating a corresponding output signal; and removing the sample and the scintillating slurry from the detection chamber.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: The present disclosure relates to a video content discovery apparatus, system, method and computer program. In one aspect of the disclosure there is provided a video content discovery module configured to receive a content query, retrieve, from one or more information sources, text that relates to the content query, process the retrieved text, at least in part using Natural Language Processing, to transform the content query to a set comprising one or more video content descriptors, and identify one or more video segments of a plurality of available video segments, using the one or more video content descriptors.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: Systems and methods for large-scale geospatial mosaic generation with image processing in a cloud computing environment. The approaches described herein specifically leverage scalable cloud computing features to facilitate highly parallel, granular image processing. Front-end image processing techniques allow for generation of a user interface that may provide automated material selection with human operator refinement. The user interface may be a web-based design that provides browse version images at lower resolutions to improve performance of the user interface and generating mosaic recipe. In turn, the mosaic recipe may be provided to back-end image processing that coordinates strip-level jobs and tile-level jobs and highly parallel fashion scalable cloud computing nodes. In turn, very large-scale mosaic images may be generated from geospatial images in computationally and cost-effective manner.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: A flexible spacer body has two opposing faces adapted to engage the inner surfaces of glazing structures to define an insulating glazing unit. The spacer body may be desiccated polymeric foam such as a silicone foam rubber or EPDM. An adhesive capable of bonding the spacer body to the glazing structure is carried by both of the faces. The adhesive may be from about 0.050 mm to about 1.524 mm thick. The adhesive material also has the properties of low argon gas and low moisture permeability. The adhesive comprises polymers where butyl rubber and/or polyisobutylene polymers together make up the majority of the polymers. The adhesive may also comprise other materials as needed to make it pressure sensitive and to impart a water resistant bond to glass glazing structures. The space assembly may include additional materials to secure the adhesive to the spacer body.