A method for generating an output image from an input image and an input text instruction that specifies a location and a modification of an edit applied to the input image using a neural network is described. The neural network includes an image encoder, an image decoder, and an instruction attention network. The method includes receiving the input image and the input text instruction; extracting, from the input image, an input image feature that represents features of the input image using the image encoder; generating a spatial feature and a modification feature from the input text instruction using the instruction attention network; generating an edited image feature from the input image feature, the spatial feature and the modification feature; and generating the output image from the edited image feature using the image decoder.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

Aspects described herein provide a computer-implemented method and system for generating topographic map data at different scales. More specifically, the topographic map data is reduced from large scale to small scale, wherein the scale of the vector features is reduced according to their geometry, feature type and attributes. In order to quickly and easily output digital maps at different scales so that a user may quickly zoom in and out of a map, different zoom levels are produced for different scales, each zoom level containing a variable amount of detail according to its scale, with larger scale zoom levels generally containing more detail than small scale zoom levels. Each zoom level is made up of one or more vector tiles representative of a geographic area, wherein the vector tiles may contain one or more vector features that are representative of objects within that geographic area, and other information related to that geographical area.

An automatic clipping technique capable of satisfactorily extracting blood vessels to be extracted is provided. A specific tissue extraction mask image which is created by extracting a specific tissue (for example, a brain) from a three-dimensional image acquired by magnetic resonance angiography and a blood vessel extraction mask image which is created by extracting a blood vessel from an area (a blood vessel search area) which is determined using a preset landmark position and the specific tissue extraction mask image are integrated to create an integrated mask. By applying the integrated mask to the three-dimensional image, a blood vessel is clipped from the three-dimensional image.

An image processing apparatus includes an input unit that inputs an image, and a processor configured to read out a program stored in a memory, and executes the program. The processor is configured to detect an intended subject from the input image by a first detection method, set an intended subject region for the detected intended subject, detect the intended subject from the input image by a second detection method different from the first detection method, and update the set intended subject region by using a detection result of the second detection method.

The present disclosure relates to image processing. The method includes acquiring at least one of a backward propagation feature of an (x+1)th video frame in a video segment or a forward propagation feature of an (x−1)th video frame in the video segment. The video segment includes N video frames, N being an integer greater than 2, and x being an integer. The method further includes deriving a reconstruction feature of the xth video frame from at least one of the xth video frame, the backward propagation feature of the (x+1)th video frame, or the forward propagation feature of the (x−1)th video frame, and deriving a target video frame corresponding to the xth video frame by reconstructing the xth video frame based on the reconstruction feature of the xth video frame. The target video frame has resolution higher than that of the xth video frame.

Texture extraction is disclosed.

Systems and methods for self-supervised depth estimation using image frames captured from a camera mounted on a vehicle comprise: receiving a first image from the camera mounted at a first location on the vehicle; receiving a second image from the camera mounted at a second location on the vehicle; predicting a depth map for the first image; warping the first image to a perspective of the camera mounted at the second location on the vehicle to arrive at a warped first image; projecting the warped first image onto the second image; determining a loss based on the projection; and updating the predicted depth values for the first image.

Disclosed are systems and associated methods for generating a regularized image from non-uniformly distributed image data based on a curve derived from the image data. The curve is used to reduce the non-uniformity in the image data without losing detail or changing the overall image. Regularizing the image includes obtaining a tree-based representation of the non-uniformly distributed image data, generating a regularization curve that models a particular distribution of the image data, and applying the regularization curve to the tree-based representation in order to select the nodes of the tree-based representation for the decimated image data to render in place of the original image data and the original image data to preserve and render as part of the regularized image. Specifically, the system render the original image data associated with leaf nodes and the decimated image data associated with parent nodes that intersect or are within the regularization curve.

A system and method of providing composite real-time dynamic imagery of a medical procedure site from multiple modalities which continuously and immediately depicts the current state and condition of the medical procedure site synchronously with respect to each modality and without undue latency is disclosed. The composite real-time dynamic imagery may be provided by spatially registering multiple real-time dynamic video streams from the multiple modalities to each other. Spatially registering the multiple real-time dynamic video streams to each other may provide a continuous and immediate depiction of the medical procedure site with an unobstructed and detailed view of a region of interest at the medical procedure site at multiple depths. A user may thereby view a single, accurate, and current composite real-time dynamic imagery of a region of interest at the medical procedure site as the user performs a medical procedure.