Disclosed is a computer-implemented method of transmitting identification information of a medical instrument. The method encompasses comparing a digital image of an instrument tray and an instrument to a digital image of just the instrument tray to determine the identity of the instrument. A characteristic geometry such as its envelope is assigned to the instrument, and a characteristic quantity of the envelope such as its aspect ratio may be used to identify the instrument. Based on determining, from the image of the instrument and the instrument tray, the relative position between those two entities, the method determines whether the instrument has been taken from the instrument tray, and accordingly instructs a medical computing system about this determination. The medical computing system may then determine whether the correct instrument has been taken from the instrument has been taken from the instrument tray, for example by comparison with medical procedure planning data.

A method for training an image segmentation model includes: acquiring target category feature information that represents category features of a training sample and a prediction sample, and associated scene feature information thereof; performing splicing processing on the target category feature information and the associated scene feature information; inputting first spliced feature information obtained by the splicing processing into an initial generation network to perform image synthesis processing; inputting a first synthesized image obtained by the synthesis processing into an initial determination network to determine authenticity; inputting the first synthesized image into a classification network of an initial image segmentation model to perform image segmentation, to obtain a first image segmentation result; and training the classification network of the initial image segmentation model based on a first image determination result, the first image segmentation result, and the target category feature information, so as to obtain a target image segmentation model.

Systems and techniques are provided for unsupervised scene-decompositional normalizing flows. An example process can include obtaining a scene-decompositional model having a normalizing flow neural network architecture. The process can include determining, based on processing data depicting multiple targets in a scene using the scene-decompositional model, a distribution of scene data as a mixture of flows from one or more background components and one or more foreground components. The process can further include identifying, based on processing the distribution of scene data using the scene-decompositional model, a target associated with the one or more foreground components and included in the data depicting the multiple targets in the scene.

Systems and techniques are provided for unsupervised scene-decompositional normalizing flows. An example process can include obtaining a scene-decompositional model having a normalizing flow neural network architecture. The process can include determining, based on processing data depicting multiple targets in a scene using the scene-decompositional model, a distribution of scene data as a mixture of flows from one or more background components and one or more foreground components. The process can further include identifying, based on processing the distribution of scene data using the scene-decompositional model, a target associated with the one or more foreground components and included in the data depicting the multiple targets in the scene.

A method of performing a command of a user on a target object by using a tag and a visual descriptor of the target object obtained through human computer interaction (HCl) is provided. The method includes obtaining a plurality of images including a target object, detecting a motion of the user manipulating the target object, based on the plurality of images, obtaining a visual descriptor of the target object including visual information for identifying the target object, obtaining a tag of the target object by receiving information related to the target object, by marking the target object, and in response to receiving an input signal corresponding to the tag, performing an operation corresponding to the input signal on the target object, based on the visual descriptor.

A method of performing a command of a user on a target object by using a tag and a visual descriptor of the target object obtained through human computer interaction (HCl) is provided. The method includes obtaining a plurality of images including a target object, detecting a motion of the user manipulating the target object, based on the plurality of images, obtaining a visual descriptor of the target object including visual information for identifying the target object, obtaining a tag of the target object by receiving information related to the target object, by marking the target object, and in response to receiving an input signal corresponding to the tag, performing an operation corresponding to the input signal on the target object, based on the visual descriptor.

An information processing apparatus comprises a first generation unit configured to generate a synthesized image in which a second image is synthesized in a closed region in a first image, and a second generation unit configured to generate learning data, the learning data including a label and the synthesized image, the label indicating an object region including a region corresponding to the closed region in the synthesized image.

An information processing apparatus comprises a first generation unit configured to generate a synthesized image in which a second image is synthesized in a closed region in a first image, and a second generation unit configured to generate learning data, the learning data including a label and the synthesized image, the label indicating an object region including a region corresponding to the closed region in the synthesized image.

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.

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.