A computer based method of obtaining a 3D image of a part of a patient's body is disclosed, based on a fraction image having a limited field-of-view and extending the field of view with information from an image of the patient's outline, obtained from a surface scan of the patient. Anatomical data from the planning image are preferably used to fill in the outline image, by means of a contour-guided deformable registration between the planning image and contour.

Methods and systems for determining a region of interest (ROI) in an image associated with a subject are provided. Slice information of multiple slices of an image are identified and multiple slice ranges are determined based on the slice information. Based on the slice ranges, multiple sub-images of the image are then determined. For each sub-image, a template and a corresponding probability graph are acquired, a registration result is determined by registering the sub-image with the template, and a ROI in the sub-image is identified based on the registration result. A ROI of the image can be determined by combining the ROIs of the sub-images.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

There is provided a method for detecting a lane marking using a processor including acquiring a drive image captured by an image capturing device of a vehicle which is running, detecting an edge corresponding to a lane marking from the acquired drive image and generating an edge image based on the detected edge, detecting a linear component based on the detected edge and generating a linearly processed edge image based on the detected linear component, detecting a lane marking point corresponding to the lane marking using the generated edge image and the linearly processed edge image, and detecting the lane marking based on the detected lane marking point.

An image modification system receives image features of a base image and an additional image. The base image and the additional image depict an apparel item displayed on a mannequin. A first feature pair from the base image and a second feature pair from the additional images are determined. A first distance is calculated between the first feature pair and a second distance is calculated between the second feature pair. Based on a ratio including the first and second distances, a matching relationship between the first and second feature pairs is determined. A pixel of the base image is identified within an image area occluded by the mannequin. Based on the matching relationship, image data is identified for a corresponding additional pixel from the additional image. A modified base image including a ghost mannequin effect is generated by modifying the pixel to include the image data of the additional pixel.

There is provided a method for detecting a lane marking using a processor including acquiring a drive image captured by an image capturing device of a vehicle which is running, detecting an edge corresponding to a lane marking from the acquired drive image and generating an edge image based on the detected edge, detecting a linear component based on the detected edge and generating a linearly processed edge image based on the detected linear component, detecting a lane marking point corresponding to the lane marking using the generated edge image and the linearly processed edge image, and detecting the lane marking based on the detected lane marking point.

Disclosed is a computer-implemented method of determining one or more position and/or orientation parameters of an anatomical structure of a body portion. The anatomical structure has a longitudinal shape defining a longitudinal axis. The method includes generating and/or reading, by a data processing system, volumetric data of at least a portion of a subject. The method further includes generating and/or reading, by the data processing system, a deformable template which provides an estimate for a location of the longitudinal axis in the portion of the subject. The method further includes matching, by the data processing system, the deformable template to the volumetric data, thereby obtaining a matched template. The matching comprises using one or more internal energy functions and one or more external energy functions for optimizing an objective function. The method further includes determining, by the data processing system, the at least one position and/or orientation parameter based on the matched template.

Geometrically constrained unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks by capturing an unlabeled image including the eye region of a user and training multiple convolutional autoencoders on the unlabeled image using an initial geometrically regularized loss function to determine multiple eye landmarks.