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: 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: 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.