This invention is related to a method for enhancing the contrast and other image quality aspects of an electronic representation of an image that is based on a multi-scale decomposition and recomposition method, wherein the image enhancement steps involve the processing of the detail images by a conversion function, which is optimized by the algorithm itself by means of optimizing the defining parameters of this conversion function by a cost-function based optimization.

This application provides a video information generation method, apparatus, and system and a storage medium. The video information generation method includes: obtaining a plurality of temporally consecutive target images; obtaining first information of a target object in the target images; and associating first information of a same target object located in different target images to generate target information. In the video information generation method provided in this application, the first information of the target object in the target images is obtained, and the first information of the same target object located in different target images is associated. In this way, target information with a relatively small amount of data can be obtained, thereby improving the efficiency of remotely viewing a video by a user.

The purpose of the present invention is to provide a fall risk evaluation system whereby risk of falling of an elderly person or other person to be managed can be easily evaluated on the basis of a captured image of daily life, instead of by a physical therapist, etc. To achieve this purpose, the present invention is a fall risk evaluation system comprising a stereo camera and a fall risk evaluation device, the fall risk evaluation device being provided with: a person authentication unit for authenticating a person to be managed who has been imaged by the stereo camera; a person tracking unit for tracking the person to be managed who is authenticated by the person authentication unit; an action extraction unit for extracting walking by the person to be managed; a feature value calculation unit for calculating a feature value of the walking extracted by the action extraction unit; an integration unit for generating integrated data obtained by integrating the outputs of the person authentication unit, the person tracking unit, the action extraction unit, and the feature value calculation unit; a fall index calculation unit for calculating a fall index value of the person to be managed, on the basis of a plurality of integrated data generated by the integration unit; and a fall risk evaluation unit for comparing the fall index value calculated by the fall index calculation unit and a threshold value to evaluate the risk of falling of the person to be managed.

A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

Aspects of the present disclosure relate to systems and methods for detecting and correcting undesirable content. A video feed may be segmented to distinguish background data from foreground data. It may be determined that a region of the background data includes a qualifying behavior. The qualifying behavior may be classified as belonging to a distracting category of data. An effect may be applied to the background data that includes the qualifying behavior to reduce an appearance of the qualifying behavior.

The present disclosure relates to a computer-implemented method for object tracking applications, preferably in Bayesian object tracking applications. The method includes the steps of providing a finite element model representing a sensor model of at least one sensor. Further, the method trains said finite element model based on observations, wherein each observation includes an output of the at least one sensor paired with a known state of at least one training object, at the time of the output of the at least one sensor, in an environment sensed by the at least one sensor. Further, the method includes the steps of obtaining signals associated with at least one tracked object in an environment sensed by the at least one sensor. Furthermore, the method determines additional outputs of the at least one sensor based on the obtained signals.

Temporal filtering operations may be reset for certain pixels within an image frame to reduce contribution from previous input frames to reduce ghosting and other artifacts. The resetting reduces the contribution to, for example, zero, either immediately or within a predetermined period of time (e.g., a certain number of frames). A decision regarding whether to reset temporal filtering for a pixel of the image frame may be based on a probability assigned to that pixel. The probability can be based on rules with one or more criteria. One example factor for adjusting probability is a confidence level regarding the temporal filtering decision for the pixel, in which the probability for a random reset of a pixel is based on the confidence level regarding the temporal filtering decision for those pixels.

According to one embodiment, an anomaly detection device includes a processor that is configured to acquire input data. The processor derives a first anomaly degree corresponding to a difference between first feature data derived from the input data using a trained deep model and second feature data derived from the input data using a trained prediction model. The processor derives a second anomaly degree corresponding to an estimated relative positional relationship between a first and second region in the image data based on the second feature data. A total anomaly degree for the input data is then calculated from the first anomaly degree and the second anomaly degree.

There is provided a method of training a machine learning model, comprising: for each set of sample medical images depicting calcification within a target anatomical structure wherein each set includes non-contrast medical image(s) and contrast enhanced medical image(s), correlating between calcifications depicted in the target anatomical structure of the contrast enhanced image(s) with corresponding calcifications depicted in the target anatomical structure of the non-contrast medical image(s), computing calcification parameter(s) for calcification depicted in the respective target anatomical structure, labelling each contrast enhanced medical image with the calcification parameter(s), and training the machine learning model on a training dataset that includes the contrast enhanced medical images of the sets, each labelled with ground truth label of a respective calcification parameter(s), for generating an outcome indicative of a target calcification parameter(s) for calcification depicted in the target anatomical structure of a target contrast enhanced medical image provided as input.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.