Disclosed is a distortion rectification method, comprising: taking a wide-angle photograph using a camera of a terminal; determining distortion regions and non-distortion regions in the wide-angle photograph; obtaining a target distortion region selected by a user; dividing the target distortion region into M grid regions of a first pre-set size, wherein M is an integer greater than or equal to one; and respectively performing distortion rectification on the M grid regions of the first pre-set size. Also disclosed is a terminal.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

Wavelet transformation is performed on first image data and second image data until a decomposition level becomes a decomposition level based on synthesis control data or the like, and first wavelet coefficient data and second wavelet coefficient data are thereby generated. An ROI coefficient related to an ROI and a non-ROI coefficient in the first wavelet coefficient data are determined on the basis of mask data and the ROI coefficient in the first wavelet coefficient data and a wavelet coefficient in the second wavelet coefficient data are synthesized with each other, and synthesized coefficient data are thereby generated. Inverse wavelet transformation is performed on the synthesized coefficient data until a decomposition level becomes a predetermined end level, and synthetic image data are thereby generated.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

A method, system, and apparatus for license plate relighting comprises collecting an image of a license plate, performing license plate recognition on the image of the license plate; calculating a confidence metric for the license plate recognition; and performing a shadow detection and relighting method if the confidence metric is below a predetermined threshold, comprising identifying a shaded region of said license plate, determining if the shaded region is actually shaded, and relighting the actually shaded region.

The disclosure describes imaging and therapy techniques comprising nanodroplets. More particularly, aspects of the disclosure relate to the use of nanodroplets to modify nanobubbles or microbubbles to provide improved imaging and/or therapeutic techniques and compositions.

Various methods and systems are provided for real-time image segmentation of medical image data. In one example, the real-time image segmentation of the medical image data may include updating an initial segmentation of the medical image data in real-time. The update may be based on a user input to a regularization brush applied to the medical image data, the user input to the regularization brush allowing modification of a volume of the initial segmentation.

Systems and methods of valve quantification are disclosed. In one embodiment, a method of mitral valve quantification is provided. The method includes generating a 3-D heart model, defining a 3-D mitral valve annulus, fitting a plane through the 3-D mitral valve annulus, measuring the distance between at least two papillary muscle heads, defining an average diameter of at least one cross section around the micro valve annulus, and determining a size of an implant to be implanted.

A method and system for facilitating identification and marking of a target in a displayed Fluoroscopic Three-Dimensional Reconstruction (F3DR) of a body region of a patient. The system includes a display and a storage device storing instructions for receiving an initial selection of the target in the F3DR, fining the F3DR based on the initial selection of the target, displaying the fined F3DR on the display, and receiving a final selection of the target in the fined F3DR via a user selection. The system further includes at least one hardware processor configured to execute said instructions. The method and instructions may also include receiving a selection of a medical device in two two-dimensional fluoroscopic images, where the medical device is located in an area of the target, and initially fining the F3DR based on the selection of the medical device.

Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions. Results of the one or more assessments of the lesion are output.