Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments of the present invention provide systems, methods, and computer storage media directed to optimizing the accuracy of local feature detection in a variety of physical environments. Homographic adaptation for facilitating personalization of local feature models to specific target environments is formulated in a bilevel optimization framework instead of relying on conventional randomization techniques. Models for extraction of local image features can be adapted according to homography transformations that are determined to be most relevant or optimal for a user's target environment.

An illustrative image descriptor generation system determines a subset of image descriptors from a plurality of image descriptors that each correspond to a different feature point included within an image. The subset of image descriptors is determined based on geometric proximity, within the image, of respective feature points of the subset of image descriptors to a feature point of a primary image descriptor. The image descriptor generation system then selects a secondary image descriptor from the subset of image descriptors and combines the primary image descriptor and the secondary image descriptor to form a composite image descriptor. Corresponding methods and systems are also disclosed.

Method for real time video processing for retouching an object in a video, comprising: providing an object in the video steam, the object being at least partially and at least occasionally presented in frames of the video; setting a degree of retouching; generating a list of at least one element of the object selected based on a request of retouching and the degree of retouching; detecting the at least one element of the object in the video and parameters of the at least one element; calculating new parameters of the at least one element according to the degree of retouching; detecting characteristic points for each of the at least one element of the object; generating a mesh based on the characteristic points for each of the at least one element of the object; tracking the at least one element of the object in the video, wherein the tracking comprises aligning the mesh for each of the at least one element with a position of the corresponding each of the at least one element; and transforming the frames of the video such that the at least one element of the object is modified by the application of the new parameters to the at least one element of the object using the mesh.

A transformed image generation unit 32 generates a transformed image by transforming an oblique image. A target region specifying unit 36 specifies a target region indicating the target in the front image based on a feature extracted from a typical portion of a document written on a target in a sample image and a feature extracted from at least a part of the front image. A verification unit 38 determines whether a target shown in the oblique image is the same as a target shown in the front image by verifying the target region in the front image and a region in the transformed image associated with the target region.

Method involving: providing an object in the video that at least partially and at least occasionally is presented in frames of a video; detecting the object in the video, wherein said detection comprises detecting feature reference points of the object; tracking the detected object in the video, wherein the tracking comprises creating a mesh that is based on the detected feature reference points of the object and aligning the mesh to the object in each frame; generating a first set of node points on the created mesh based on a request for changing proportions; generating a second set of node points based on the first set of node points; and transforming the frames of the video in such way that the object's proportions are transformed in accordance with the second set of the node points using the mesh.

A computer implemented method of triggering events in a video, the method comprising: providing a list of objects with their states and corresponding events in video such that each state from the list triggers at least one event of the corresponding events, wherein each object from the list has at least one state triggering at least one event of the corresponding events from the list in video; detecting at least one object from the list that at least partially and at least occasionally is presented in frames of the video; tracking the at least one object and its state; triggering at least one event of the corresponding events from the list in video in case the state of the at least one object matches with one of its states from the list.

A path and/or orientation of object approaching an athlete is tracked using two or more cameras. At least two sets of images of the object are obtained using at least two different cameras having different positions. Motion regions within images are identified, and candidate locations in 2D space of the object are identified within the motion region(s). Based thereon, a probable location in 3D space of the identifiable portion is identified, for each of a plurality of instants during which the object was approaching. A piecewise 3D trajectory of at least the identifiable portion of the object is approximated from the probable locations in 3D space of the object for multiple instants during which the object was approaching the athlete. A graphical representation of the 3D trajectory of the object is incorporated into at least one of the sets of images.

A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.