Described is a solution for an unlabeled target domain dataset challenge using a domain adaptation technique to train a neural network using an iterative 3D model fitting algorithm to generate refined target domain labels. The neural network supports the convergence of the 3D model fitting algorithm and the 3D model fitting algorithm provides refined labels that are used for training of the neural network. During real-time inference, only the trained neural network is required. A convolutional neural network (CNN) is trained using labeled synthetic frames (source domain) with unlabeled real depth frames (target domain). The CNN initializes an offline iterative 3D model fitting algorithm capable of accurately labeling the hand pose in real depth frames. The labeled real depth frames are used to continue training the CNN thereby improving accuracy beyond that achievable by using only unlabeled real depth frames for domain adaptation.

An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

A computer-implemented system and method of generating a graph representation of a scene comprising receiving sensor data representative of a perceived scene captured with a sensor; defining a plurality of nodes based on the received sensor data; creating a densely connected graph by connecting each node to a number of nearest neighbour nodes; predicting, for each pair of connected nodes of the densely connected graph, at least a node probability, wherein the node probability represents a probability that the pair of connected nodes represent the same object; and generating a graph representation of the perceived scene based at least on the densely connected graph, and the predicted node probability. The method may also be applied to two or more observations of a scene.

A computer-implemented system and method of generating a graph representation of a scene comprising receiving sensor data representative of a perceived scene captured with a sensor; defining a plurality of nodes based on the received sensor data; creating a densely connected graph by connecting each node to a number of nearest neighbour nodes; predicting, for each pair of connected nodes of the densely connected graph, at least a node probability, wherein the node probability represents a probability that the pair of connected nodes represent the same object; and generating a graph representation of the perceived scene based at least on the densely connected graph, and the predicted node probability. The method may also be applied to two or more observations of a scene.

An unsupervised image segmentation method includes: performing a superpixel segmentation on an image containing a target object to acquire a plurality of superpixel sets, each superpixel set corresponding to a respective superpixel node; generating an undirected graph according to superpixel nodes; determining foreground superpixel nodes and background superpixel nodes in the undirected graph according to a first label set corresponding to the plurality of superpixel nodes; generating a minimization objective function according to the foreground superpixel nodes and the background superpixel nodes; segmenting the undirected graph according to the minimization objective function to acquire a foreground part and a background part and to generate a second label set; and performing an image segmentation on the image according to a comparison result of the first label set and the second label set.

A computer implemented method of processing image data by an image processing system comprising a directed acyclic graph of nodes for receiving and processing image data is disclosed. The method comprises: at a compute node in the graph: receiving image data; performing an image processing operation on the image data to produce a compute node output based on the data; and transmitting the compute node output to another node in the graph. The method also comprises, at a control node in the graph: receiving a control node input, wherein the control node input is the image data or is based on the image data; and, if the control node determines that a control condition is satisfied, transmitting the control node input to another node in the graph. A computer implemented image processing system is also disclosed.

A computer implemented method of processing image data by an image processing system comprising a directed acyclic graph of nodes for receiving and processing image data is disclosed. The method comprises: at a compute node in the graph: receiving image data; performing an image processing operation on the image data to produce a compute node output based on the data; and transmitting the compute node output to another node in the graph. The method also comprises, at a control node in the graph: receiving a control node input, wherein the control node input is the image data or is based on the image data; and, if the control node determines that a control condition is satisfied, transmitting the control node input to another node in the graph. A computer implemented image processing system is also disclosed.

The medical image processing apparatus disclosed herein includes processing circuitry configured to acquire a medical image for setting identification information for identifying a plurality of regions in the medical image, determine a plurality of seed points in the medical image, select one of the plurality of seed points on a shortest path from a first node, the shortest path being a path in which a sum of energies between pixels in the medical image is minimized and the first node being a pixel of the medical image for which the identification information is not set, and allocate the identification information.

The medical image processing apparatus disclosed herein includes processing circuitry configured to acquire a medical image for setting identification information for identifying a plurality of regions in the medical image, determine a plurality of seed points in the medical image, select one of the plurality of seed points on a shortest path from a first node, the shortest path being a path in which a sum of energies between pixels in the medical image is minimized and the first node being a pixel of the medical image for which the identification information is not set, and allocate the identification information.