Abstract: An image processing method includes defining relations between entities of a target of which a motion is to be predicted from an image of a first time point based on a feature vector of the entities, estimating a dynamic interaction between the entities at the first time point based on the defined relations between the entities, predicting a motion of the entities changing at a second time point based on the estimated dynamic interaction, and outputting a result to which the motion predicted at the second time point is applied.

Abstract: Panoptic segmentation forecasting predicts future positions of foreground objects and background objects separately. An egomotion model may be implemented to estimate egomotion of the camera. Pixels in frames of captured video are classified between foreground and background. The foreground pixels are grouped into foreground objects. A foreground motion model forecasts motion of the foreground objects to a future timestamp. A background motion model backprojects the background pixels into point clouds in a three-dimensional space. The background motion model predicts future positions of the point clouds based on egomotion. The background motion model may further generate novel point clouds to fill in occluded space. With the predicted future positions, the foreground objects and the background pixels are combined into a single panoptic segmentation forecast.

Abstract: An image processing method includes defining relations between entities of a target of which a motion is to be predicted from an image of a first time point based on a feature vector of the entities, estimating a dynamic interaction between the entities at the first time point based on the defined relations between the entities, predicting a motion of the entities changing at a second time point based on the estimated dynamic interaction, and outputting a result to which the motion predicted at the second time point is applied.

Abstract: A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.

Abstract: A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume.

Abstract: A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.

Abstract: A method and apparatus for detecting multiple anatomical landmarks in a 3D volume. A first anatomical landmark is detected in a 3D volume using marginal space learning (MSL). Locations of remaining anatomical landmarks are estimated in the 3D volume based on the detected first anatomical landmark using a learned geometric model relating the anatomical landmarks. Each of the remaining anatomical landmarks is then detected using MSL in a portion of the 3D volume constrained based on the estimated location of each remaining landmark. This method can be used to detect the anatomical landmarks of the crista galli (CG), tip of the occipital bone (OB), anterior of the corpus callosum (ACC), and posterior of the corpus callosum (PCC) in a brain magnetic resonance imaging (MRI) volume.

Abstract: A method for carrying out well construction, repair and abandonment operations, the method involves introducing a resin into a well and curing the same to form a seal, plug or connection, wherein the cured resin is expanded to at least the volume occupied by the resin prior to curing (compensating shrinkage), by cooling the well and curing the resin at a reduced temperature and subsequently allowing the well to reach its static bottom hole temperature. A method is also disclosed for analyzing the setting time, elastic properties, shrinkage/expansion, compressibility or coefficient of thermal expansion of thermosetting resins or oil well cements under simulated reservoir pressure and temperature conditions, which involves introducing a sample of a thermosetting resin or oil well cement into a pressure vessel that is equipped to provide the pressure and register the volume change to the analyzer used by this method.

Abstract: This invention relates to a method for carrying out well construction, repair and abandonment operations, which method involves introducing a resin into a well and curing the same to form a seal, plug or connection, wherein the cured resin is expanded to at least the volume occupied by the resin prior to curing (compensating shrinkage), by cooling the well and curing the resin at a reduced temperature and subsequently allowing the well to reach its static bottom hole temperature. This invention also relates to a method for analyzing the setting time, elastic properties, shrinkage/expansion, compressibility or coefficient of thermal expansion of thermosetting resins or oil well cements under simulated reservoir pressure and temperature conditions, which comprises a introducing a sample (7) of a thermosetting resin or oil well cement into a pressure vessel (3) that is equipped with a means(9) to provide the pressure and register the volume change; and to the analyzer used by this method.