Abstract:
Provided are a device and method for estimating a pose of an object. The method includes inputting a labeled source image and an unlabeled target image to a recognition model for generating training data, training the recognition model to generate object information of the unlabeled target image, determining the generated object information to be a pseudo label of the unlabeled target image, and training a pose estimation model for estimating a pose of an object by inputting the pseudo-labeled target image and the labeled source image to the pose estimation model.
Abstract:
A control method of a gateway communicating with at least one sensor and a service server is provided. The control method may include receiving, from the at least one sensor, first information comprising at least one of bioinformation, disaster prevention information, and public information, transmitting the first information to the service server when the first information meets a first standard, or processing the first information in a data format of the first standard and transmitting the processed first information to the service server when the first information does not meet the first standard, receiving, from the service server, a control command to control the at least one sensor, and transmitting the control command to a sensor corresponding to the control command.
Abstract:
Provided are a device and method for estimating a pose of an object. The method includes inputting a labeled source image and an unlabeled target image to a recognition model for generating training data, training the recognition model to generate object information of the unlabeled target image, determining the generated object information to be a pseudo label of the unlabeled target image, and training a pose estimation model for estimating a pose of an object by inputting the pseudo-labeled target image and the labeled source image to the pose estimation model.
Abstract:
A method and apparatus for recognizing a three-dimensional (3D) object based on deep learning are provided. An object recognition apparatus constructs a data set including a virtual image and a real image, in which the data set includes labeled data corresponding to the virtual image and the real image, and unlabeled data corresponding to the virtual image and the real image. The object recognition apparatus inputs the data set to a recognition model for pre-trained object recognition based on self-supervised learning to perform the object recognition and acquire object information according to the object recognition.
Abstract:
Provided is a miniaturized photo-acoustic probe for a clinical image capable of effectively measuring a photo-acoustic signal by making an ultrasonic axis and an optical axis parallel. The photo-acoustic probe for a clinical image includes a laser generator configured to generate a laser beam, an ultrasound transducer disposed to be parallel to the laser generator and configured to analyze ultrasound output from an object, first and second reflectors configured to receive ultrasound generated in an axis identical to that of the laser beam generated by the laser generator, and a medium material configured to allow the laser to be transmitted from the first reflector to the object and increase ultrasound reflectivity of the first and the second reflector.