Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for detecting data anomalies on a device. For instance, the method may include: receiving an initial data measurement transmitted by the device, wherein the initial data measurement includes a measurement of data stored in the device using a unique key associated with the device; transmitting a request for a subsequent data measurement of data stored in the device; receiving the subsequent data measurement transmitted by the device; comparing the subsequent data measurement to the initial data measurement; and determining whether an anomaly exists in the data stored in the device based on the comparison.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for detecting data anomalies on a device. For instance, the method may include: receiving an initial data measurement transmitted by the device, wherein the initial data measurement includes a measurement of data stored in the device using a unique key associated with the device; transmitting a request for a subsequent data measurement of data stored in the device; receiving the subsequent data measurement transmitted by the device; comparing the subsequent data measurement to the initial data measurement; and determining whether an anomaly exists in the data stored in the device based on the comparison.

Abstract: A process identifies features in a probe image and a donor image. A similarity measure matches the features in the probe image with features in the donor image, and forms pairs of matched features. The process then forms clusters of the pairs based on the pairs occupying a similar location in the probe image, and verifies that the clusters in the probe image are good fits for corresponding features in the donor image. Locations of the clusters and locations of the corresponding features are marked, and the extent to which the clusters and the corresponding features represent the same semantic class. The process calculates a score based on clusters having the good fit and the clusters in the first digital image having a similar semantic interpretation as the corresponding cluster in the second digital image.

Abstract: The present disclosure relates to devices, systems, and methods of image pre-processing for object recognition. One method includes analyzing data about an image to determine one or more characteristics of the image that indicate whether one or more enhancements should be performed on the data, selecting one or more enhancements to consider applying to the data based on the one or more determined characteristics, analyzing the data to determine whether performing each of the selected enhancements will improve the image quality, determining which one or more enhancements to select to perform on the data based on the analysis of whether performing the selected enhancements will improve the image quality, and performing the selected enhancements on the data to improve the image quality.