Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, detection of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them. The system is configured to learn to recognize and detect any number of various toy elements by training a deep convolutional neural network.

Abstract: A recognition system for recognizing real-world toy objects from one or more images having an image capturing device and one or more processors. The processor implements a detection module, a recognition module, and a user experience module, and is configured to capture an image of a real-world scene, and detect one or more regions of interest in the image. The recognition system is configured to generate at least one part-image from the captured image, each part image including at least one of the one or more detected regions of interest, and feed the generated part-image to the recognition module. The recognition system is configured to recognize a real-world toy object in the part-image, the real-world toy object comprising at least one toy construction element, and provide a digital representation of the recognized real-world toy object.

Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, detection of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them. The system is configured to learn to recognize and detect any number of various toy elements by training a deep convolutional neural network.

Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, recognition of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them.

Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, recognition of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them.

Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, recognition of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them. The system is configured to learn to recognize and detect any number of various toy elements by training a deep convolutional neural network.

Abstract: Embodiments relate to a deployment of a master radio in conjunction with one or more slave radios. The master radio coordinates with a UE, or transmitter, to transmit a particular known set of wireless signals in a defined time window over a dedicated wireless connection between the UE and the master radio. The master radio separately coordinates with the one or more slave radios to monitor for and measure the dedicated wireless connection during the defined time window (e.g., to obtain AoA measurements, signal strength measurements, etc.). The measurements can be reported to the master radio or a separate measurement processing unit to facilitate a location determination of the UE.

Abstract: Embodiments relate to a deployment of a master radio in conjunction with one or more slave radios. The master radio coordinates with a UE, or transmitter, to transmit a particular known set of wireless signals in a defined time window over a dedicated wireless connection between the UE and the master radio. The master radio separately coordinates with the one or more slave radios to monitor for and measure the dedicated wireless connection during the defined time window (e.g., to obtain AoA measurements, signal strength measurements, etc.). The measurements can be reported to the master radio or a separate measurement processing unit to facilitate a location determination of the UE.

Abstract: System and method for automatic computer aided optical recognition of toys, for example, construction toy elements, recognition of those elements on digital images and associating the elements with existing information is presented. The method and system may recognize toy elements of various sizes invariant of toy element distance from the image acquiring device for example camera, invariant of rotation of the toy element, invariant of angle of the camera, invariant of background, invariant of illumination and without the need of predefined region where a toy element should be placed. The system and method may detect more than one toy element on the image and identify them. The system is configured to learn to recognize and detect any number of various toy elements by training a deep convolutional neural network.

Abstract: A location device (e.g., realized at one or more of a mobile device and/or a location server) includes a wireless transceiver, one or more location sensors and a floor-plan of a venue. The location device determines a plurality of hypothetical locations of a mobile device in the venue in response to data sets provided by the one or more location sensors. The data sets are associated with obstacle indication values calculated for the venue or for regions of the venue including locations corresponding to the data sets. The obstacle indication values indicate the proximity or orientation of the obstacle edges in the region or in the venue. The location device refines the one or more hypothetical locations based on the obstacle indication values to provide an estimate of the current location of the mobile device.