Described are methods and systems for training a system for detecting anomalies in images of documents in a class of documents. A plurality of training document images of training documents in a class of documents are obtained. For each training document image, the training document image is segmented into a plurality of region of interest (ROI) images, each ROI image corresponding to a respective ROI of the training document. For each ROI image, a plurality of transformations are applied to the ROI image to generate respective transform-specific features for the ROI image and respective transform-specific anomaly scores from the transform-specific features. Based on the respective anomaly scores of the plurality of training document images, a transform-specific threshold is computed for each transformation to separate document images containing an anomaly from document images not containing an anomaly.

A method for measuring distorted illumination patterns and correcting image artifacts in structured illumination microscopy. The method includes the steps of generating an illumination pattern by interfering multiple beams, modulating a scanning speed or an intensity of a scanning laser, or projecting a mask onto an object; taking multiple exposures of the object with the illumination pattern shifting in phase; and applying Fourier transform to the multiple exposures to produce multiple raw images. Thereafter, the multiple raw images are used to form and then solve a linear equation set to obtain multiple portions of a Fourier space image of the object. A circular 2-D low pass filter and a Fourier Transform are then applied to the portions. A pattern distortion phase map is calculated and then corrected by making a coefficient matrix of the linear equation set varying in phase, which is solved in the spatial domain.

A data item is identified on a device. A neural network that includes an adversarial transformation subnetwork is applied to the data item to generate a modified data item. Output indicative of the modified data item is caused to be presented on the device. The neural network further comprises an encoder and a decoder. The neural network is trained in at least two stages. At least one of the encoder and the decoder is trained in a first stage and the adversarial transformation subnetwork is trained in a second stage.

This invention relates generally to the detection of objects, such as stents, within intraluminal images using principal component analysis and/or regional covariance descriptors. In certain aspects, a training set of pre-defined intraluminal images known to contain an object is generated. The principal components of the training set can be calculated in order to form an object space. An unknown input intraluminal image can be obtained and projected onto the object space. From the projection, the object can be detected within the input intraluminal image. In another embodiment, a covariance matrix is formed for each pre-defined intraluminal image known to contain an object. An unknown input intraluminal image is obtained and a covariance matrix is computed for the input intraluminal image. The covariances of the input image and each image of the training set are compared in order to detect the presence of the object within the input intraluminal image.

Described herein is a system and method of controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient. The computer-implemented method comprises obtaining a plurality of real-time image data corresponding to 2-dimensional (2D) magnetic resonance imaging (MRI) images including at least a portion of the region, performing 2D motion field estimation on the plurality of image data, approximating a 3-dimensional (3D) motion field estimation, including applying a conversion model to the 2D motion field estimation, determining at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation, and controlling the treatment of at least a portion of the region using the determined at least one change.

In accordance with the teachings described herein, systems and methods are provided for generating images for use in systems, e.g., imaging systems. The method includes receiving at least a first set of images, providing a first registration, providing a display, and displaying a first image on said display. Further, the method includes providing a user interface, providing a second registration, and displaying a second image in said user interface. Further, the systems include an image database, a display, and a registration engine. The registration engine includes software instructions stored in at least one memory device and executable by one or more processors.

An image processing device includes a processor including hardware, the processor being configured to calculate comparison result information through a comparison process performed on the results of a census transform process performed on a first image and the results of the census transform process performed on a second image, set weight information, calculate a cost using the comparison result information and the weight information, calculate the amount of disparity using the cost, set a first weight having as low degree of contribution to the cost on a neighboring pixel which is determined to be affected by noise to a large extent among a plurality of neighboring pixels, and set a second weight having a high degree of contribution to the cost on a neighboring pixel which is determined to be affected by noise to a small extent among the plurality of neighboring pixels.

The invention relates to a microscope and a method for producing 3D images of various transparent or semi-transparent samples, fundamentally comprising: causing a relative movement according to the detection direction between the sheet of light and the sample while maintaining a constant angle of acquisition; producing, for said angle of acquisition, a single 2D projection image formed by a representative parameter for each pixel; modifying the angle of acquisition by means of a relative rotation between the sheet of light and the sample, combined with a relative vertical translation between the sheet of light and the sample, and repeating the previous steps; and generating a 3D image of each of the samples from the set of 2D projection images that are produced.

A video coding device may be configured to perform directional Bi-directional optical flow (BDOF) refinement on a coding unit (CU). The device may determine the direction in which to perform directional BDOF refinement. The device may calculate the vertical direction gradient difference and the horizontal direction gradient difference for the CU. The vertical direction gradient difference may indicate the difference between the vertical gradients for a first reference picture and the vertical gradients for a second reference picture. The horizontal direction gradient difference may indicate the difference between the horizontal gradients for the first reference picture and the horizontal gradients for the second reference picture. The video coding device may determine the direction in which to perform directional BDOF refinement based on the vertical direction gradient difference and the horizontal direction gradient difference. The video coding device may perform directional BDOF refinement in the determined direction.

An image processing method and related apparatus. An image is decomposed into spatial frequency components images. The spatial frequency component images are normalized relative to specific noise models, remapped by a noise reduction function and are then combined to obtain a noise reduced version of the image.