An image processing system includes a processor and optical flow (OF) determination logic for quantifying relative motion of a feature present in a first frame of video and a second frame of video that provide at least one of temporally and spatially ordered images with respect to the two frames of video. The OF determination logic configures the processor to implement performing OF estimation between the first frame and second frame using a pyramidal block matching (PBM) method to generate an initial optical flow (OF) estimate at a base pyramid level having integer pixel resolution, and refining the initial OF estimate using at least one pass of a modified Lucas-Kanade (LK) method to provide a revised OF estimate having fractional pixel resolution.

An object of the present invention is to improve the image quality of images obtained during travelling even in inferior environmental conditions. A camera 101-1 is mounted in a train 1-1, and a camera 101-2 is mounted in a train 1-2. Each of the camera 101-1 and the camera 101-2 images the same physical object at different times while moving along the same track 5. A processing apparatus 2 generates a synthetic image 7 by synthesizing images related to the same physical object imaged by the cameras 101-1 and 101-2 while moving along the same track 5, and displays the same on a display screen 2A.

Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Provided are an image processing apparatus, an image processing method, and a storage medium that can distinguish an anomaly while reducing influence of an individual difference of images. The image processing apparatus includes: a generation unit that uses a part of an inspection image including an inspection target to generate an estimation image including at least a predetermined region of the inspection target; a comparison unit that compares the estimation image generated by the generation unit with the inspection image; and an output unit that outputs a comparison result obtained by the comparison unit.

A method for applying compensation to a display screen to reduce MURA effects in the display includes: collecting a display image of the display panel and obtaining panel parameters, the panel parameters comprising a greyscale level values and a luminance value of each pixel in the display image. Regions containing a foreign object are recognized and removed, the blanked regions being infilled. A Gramma value of the display panel is calculated according to the panel parameters and a Gamma curve determined, luminance value of each region of the display panel being obtained according to the Gamma curve. An ideal quadric surface for the display panel is obtained according to the luminance value of each region, a compensation parameter is calculated according to the ideal quadric surface, and compensation applied accordingly. A system and electronic device are also disclosed.

System and method for object detection. Images from cameras are provided to an inference engine to detect objects in real time, providing the images to an inference engine to detect the non-background and background pixels of the objects in the images, determining the position and size of the objects in the images based on contemporaneously gathered LiDAR data and the relationship of non-background to background pixels.

A method for creating, in particular acquiring and reconstructing, a sequence of magnetic resonance (MR) images of an object (1), said sequence of MR images representing a series of cross-sectional slices (2) of the object (1), comprises (a) providing a series of sets of image raw data including an image content of the MR images to be reconstructed, said image raw data being collected with at least one radiofrequency receiver coil of a magnetic resonance imaging (MRI) device, wherein each set of image raw data includes a plurality of data samples being generated in an imaging plane with a gradient-echo sequence that spatially encodes an MRI signal received with the at least one radiofrequency receiver coil using a non-Cartesian k-space trajectory, each set of image raw data comprises a set of homogeneously distributed lines in k-space with equivalent spatial frequency content, the lines of each set of image raw data cross the center of k-space and cover a continuous range of spatial frequencies, the positions of the lines of each set of image raw data differ in successive sets of image raw data, and the number of lines of each set of image raw data is selected such that each set of image raw data is undersampled below a sampling rate limit defined by the Nyquist-Shannon sampling theorem, and (b) subjecting the sets of image raw data to a regularized nonlinear inverse reconstruction process to provide the sequence of MR images, wherein each of the MR images is created by a simultaneous estimation of a sensitivity of the at least one receiver coil and the image content and in dependency on a difference between a current estimation of the sensitivity of the at least one receiver coil and the image content and a preceding estimation of the sensitivity of the at least one receiver coil and the image content, wherein said cross-sectional slices (2) of the object (1) are contiguous cross-sectional slices (2) with a predetermined slice thickness, each set of said image raw data represents one of said contiguous cross-sectional slices (2), and the position of each cross-sectional slice is shifted by a slice shift A perpendicular to the imaging plane in order to cover a volume of the object (1).

A method of recursive filtering of image data includes receiving sequentially a plurality of original pixel values, multiplying an original pixel value of a current pixel of the plurality of pixel values by a dynamically varying recursion coefficient, adding recursively filtered pixel values from left and right neighbors of the current pixel, retrieved from a memory buffer holding filtered pixel data from a previous image line, multiplying the added recursively pixel values by 1 minus the dynamically varying recursion coefficient, adding the two multiplication results together to yield a filtered pixel value for the current pixel, writing the filtered pixel value for the current pixel back into the memory buffer, and displaying the filtered pixel value on a display.

Median values for a stream of received data values in a data processing system (e.g. an image processing system) are determined. A first median value of the received data values within a first subset of data values of the received stream is determined, and intermediate data used for determining the first median value is stored. The stored intermediate data is used to determine a median value of the received data values within a second subset of data values of the received stream, wherein the second subset at least partially overlaps with the first subset. The determined median values are outputted for use in the data processing system, e.g. for further processing.

Described herein are means for performing self-supervised visual representation learning using order and appearance recovery on a vision transformer. An exemplary system having a processor and memory is specially configured to execute instructions including: receiving medical image training data; selecting a medical image; generating a first perturbed image by applying local pixel shuffling and other image perturbations and outputting a first patchified perturbed image; generating a second randomized patchified image by patchifying and applying a random permutation to the original image; inputting the first patchified perturbed image and the second randomized patchified image into first and second transformer encoders which each generate and then share first and second generated weights through the recovery of both and patch order appearance from each image; and outputting a pre-trained AI model to perform medical image diagnosis on a new medical image absent from the training data input received by the system.