Abstract: Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including; a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaining state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

Abstract: A method for producing zeolite embedded polyolefin films for packaging polyurethane foams also embedded with zeolite.

Abstract: Embodiments relate to systems, methods and computer readable media for parking monitoring in an urban area by image processing operations. Embodiments perform parking monitoring by capturing images of an urban area, comparing captured images with reference images to determine location and parking conditions. Embodiments processes captured images to detect licence plates, vehicles or parking signs to determine compliance of vehicles with parking conditions.

Abstract: Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

Abstract: Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

Abstract: Embodiments relate to systems and methods for gaming monitoring. In particular, embodiments relate to systems and methods for gaming monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.

Abstract: Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaining object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

Abstract: Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

Abstract: Embodiments relate to systems, methods and computer readable media for parking monitoring in an urban area by image processing operations. Embodiments perform parking monitoring by capturing images of an urban area, comparing captured images with reference images to determine location and parking conditions. Embodiments processes captured images to detect licence plates, vehicles or parking signs to determine compliance of vehicles with parking conditions.

Abstract: Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaming state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

Abstract: Embodiments relate to systems and methods for gaining monitoring. In particular, embodiments relate to systems and methods for gaining monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.

Abstract: Embodiments relate to systems and methods for gaming monitoring. In particular, embodiments relate to systems and methods for gaming monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.

Abstract: Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaming state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

Abstract: Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaming state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

Abstract: Embodiments relate to systems and methods for gaining monitoring. In particular, embodiments relate to systems and methods for gaining monitoring based on machine learning processes configured to analyse captured images to identify or detect game objects and game events to monitor games.

Abstract: Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaming state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

Abstract: A system for identifying a vehicle. A camera obtains at least one image of a vehicle. An image processor derives from the image a first sub-image and a second sub-image distinct from the first sub-image, extracts from the first sub-image a first set of image features, and extracts from the second sub-image a second set of image features. The image processor matches the first set of image features to corresponding image features derived from a previously obtained image of a vehicle to produce a first matching score, and also matches the second set of image features to corresponding image features derived from a previously obtained image of a vehicle to produce a second matching score. The image processor then fuses the first matching score and the second matching score to produce a fused score which indicates whether the at least one image is of the same vehicle as the previously obtained image.

Abstract: A method and system for identifying one or more features represented in a plurality of sensor acquired data sets is described. The method and apparatus is particularly useful in automatic license plate recognition applications, where the sensor acquired data sets are data obtained from one or more digital cameras. This is achieved by determining a first probability of the identity of the one or more features (e.g., alphanumeric characters) from a first one of the data sets; determining a second probability of the identity of the one or more features from a second one of the data sets; and, using data fusion techniques, fusing the determined first and second probabilities to provide a fused probability. This fused probability is used to identify the one or more features from data sets.

Abstract: A method and system for identifying one or more features represented in a plurality of sensor acquired data sets is described. The method and apparatus is particularly useful in automatic license plate recognition applications, where the sensor acquired data sets are data obtained from one or more digital cameras. This is achieved by determining a first probability of the identity of the one or more features (eg alphanumeric characters) from a first one of the data sets; determining a second probability of the identity of the one or more features from a second one of the data sets; and, using data fusion techniques, fusing the determined first and second probabilities to provide a fused probability. This fused probability is used to identify the one or more features from data sets.

Abstract: A method and system for identifying one or more features represented in a plurality of sensor acquired data sets is described. The method and apparatus is particularly useful in automatic license plate recognition applications, where the sensor acquired data sets are data obtained from one or more digital cameras. This is achieved by determining a first probability of the identity of the one or more features (e.g., alphanumeric characters) from a first one of the data sets; determining a second probability of the identity of the one or more features from a second one of the data sets; and, using data fusion techniques, fusing the determined first and second probabilities to provide a fused probability. This fused probability is used to identify the one or more features from data sets.