Systems and methods for semantic labeling of point clouds using images. Some implementations may include obtaining a point cloud that is based on lidar data reflecting one or more objects in a space; obtaining an image that includes a view of at least one of the one or more objects in the space; determining a projection of points from the point cloud onto the image; generating, using the projection, an augmented image that includes one or more channels of data from the point cloud and one or more channels of data from the image; inputting the augmented image to a two dimensional convolutional neural network to obtain a semantic labeled image wherein elements of the semantic labeled image include respective predictions; and mapping, by reversing the projection, predictions of the semantic labeled image to respective points of the point cloud to obtain a semantic labeled point cloud.

A computer-implemented method for calculating Scalable Attributed Graph Embedding for Large-Scale Graph Analytics that includes computing a node embedding for a first node-attributed graph in a node embedded space. One or more random attributed graphs is generated in the node embedded space. A graph embedding operation is performed using a dissimilarity measure between one or more raw graphs and the one or more generated random graphs, and an edge-attributed graph into a second node-attributed graph using an adjoint graph.

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

A moving object detection device includes: an image capturing unit with which a vehicle is equipped, and which is configured to obtain a captured image by capturing a view in a travel direction of the vehicle; a calculation unit configured to calculate, for each of first regions which are unit regions of the captured image, a first motion vector indicating movement of an image in the first region; an estimation unit configured to estimate, for each of one or more second regions which are unit regions each including first regions, a second motion vector using first motion vectors, the second motion vector indicating movement of a stationary object which has occurred in the captured image due to the vehicle traveling; and a detection unit configured to detect a moving object present in the travel direction, based on a difference between a first motion vector and a second motion vector.

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

At least one vascular tree supplying at least a part of the hollow organ in the computed tomography dataset is segmented, and a tree structure up to an order possible based on the blood vessel segmentation result is determined from a blood vessel segmentation result. Perfusion information for each edge in the tree structure is assigned as at least one of the computed tomography data assigned to the blood vessel segment or at least one value derived therefrom. Adjacent hollow organ segments of the hollow organ are defined based on supply by adjacent blood vessels in the tree structure, and the tree structure and the perfusion information are analyzed to determine hemodynamic information to assign to hollow organ segments. At least a part of the hemodynamic information in at least one of the computed tomography dataset or a visualization dataset derived therefrom is then visualized.