A system and method for detecting motion with respect an object includes providing a processor and at least one video sensor; synchronizing the at least one video sensor to a dynamic event associated with the object; recording, by the at least one video sensor, a plurality of data sets including the object, each data set of the plurality of data sets being synchronized with respect to the dynamic event associated with the object; averaging, by the processor, the plurality of data sets to provide an averaged synchronized data set; and calculating, by the processor, a motion with respect to the object based on the averaged synchronized data set.

According to one embodiment, a method for displaying an index value in generation of a mask pattern verification model includes: calculating a first index value using a plurality of images; estimating a model on the basis of the first index value and pattern information; calculating a second index value using the model; and displaying at least one of the first index value and the second index value.

A system and method for processing an input video while maintaining temporal consistency across video frames is provided. The method includes converting the input video from a first frame rate to a second frame rate, wherein the second frame rate is a faster frame rate than the first frame rate; generating processed frames of the input video at the second frame rate; and aggregating the processed frames using temporal sliding window aggregation to yield a processed output video at a third frame rate.

A magnetic resonance (MR) imaging method of correcting nonuniformity in diffusion-weighted (DW) MR images of a subject is provided. The method includes applying a DW pulse sequence along a plurality of diffusion directions with one or more numbers of excitations (NEX), and acquiring a plurality of DW MR images of the subject along the plurality of diffusion directions with the one or more NEX. The method also includes deriving a reference image and a base image based on the plurality of DW MR images, generating a nonuniformity factor image based on the reference image and the base image, and combining the plurality of DW MR images into a combined image. The method also includes correcting nonuniformity of the combined image using the nonuniformity factor image, and outputting the corrected image.

This disclosure relates to a device for digital signal processing, particularly video image processing. The device obtains image data comprising a plurality of pixels. The image data comprises a plurality of sequentially captured images. The device estimates, for a target image, a set of backward motion vector fields (backward MVFs) based on the target image, and a first set of images captured before the target image. The device further estimates a set of forward MVFs based on the target image and a second set of images captured after the target image. Depending on the estimating for the target image, the device generates an output image based on a merging procedure of the target image and the first set of images and the set of backward MVFs, and/or the second set of images and the set of forward MVFs.

Disclosed herein is a machine learning application for automatically reading filled-in forms. There are multiple steps involved in using a computer to accurately read a handwritten form. First, the system identifies the form. Second, the system identifies what parts of the form are important. Third, the important parts are extracted as image data (known as shreds). Finally, fourth, the system interprets the shreds. This application is focused on steps two and three of that overall process. The disclosed techniques relate to training a machine learning system on a given series of forms such that when provided future filled-in forms within that series, the system is able to extract the portions of the filled-in form that are important/relevant.

Systems, computer-readable media, methods, and approaches described herein may identify delivery and/or pickup of packages. For example, packages may be identified within the areas captured by images and/or video. Based on the identification of the packages, it may be determined whether the package was delivered or picked up. A notification may be initiated that indicates that a package has been delivered and/or picked up.

Examples are disclosed that relate to motion compensation on a single photon avalanche detector (SPAD) array camera. One example provides a method enacted on an imaging device comprising a SPAD array camera and a motion sensor, the SPAD array camera comprising a plurality of pixels. The method comprises acquiring a plurality of subframes of image data. Each subframe of image data comprises a binary value for each pixel. Based upon motion data from the motion sensor, the method further comprises determining a change in pose of the imaging device between adjacent subframes, applying a positional offset to a current subframe based upon the motion data to align a location of a stationary imaged feature in the current subframe with a location of the stationary imaged feature in a prior subframe to create aligned subframes, summing the aligned subframes to form an image, and outputting the image.

Exemplary embodiments are directed to a system for selective replacement of an object in an image. The system includes an interface configured to receive as input an original image and a background image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, generate a filtered original image including the original image without the one or more objects, generate a modulated background image including a replacement background based on the neural network mask, and generate a combined image including the filtered original image combined with the modulated background image.

A medical image processing apparatus includes processing circuitry. The processing circuitry generates a plurality of first medical images by applying a plurality of first machine learning models having different elements to a set of raw data, or applying a first machine learning model to a set of raw data a plurality of times while changing elements. The processing circuitry outputs a second medical image and a first reliability relating to the second medical image based on the first medical images.