A system includes a lighting module, a processing module, and photovoltaic units. Each of the photovoltaic units receives light reflected off a body portion which is illuminated by light from the lighting module, and converts light energy of the reflected light into electricity. The processing module stores modes each of which specifies a code set. When one of the modes is selected, the processing module activates the lighting module to emit light based on the code set specified by the mode thus selected. The processing module converts electrical quantities measured individually for the photovoltaic units into respective code parameters, and composes an identification code using the code parameters.

A device includes an image acquisition means for acquiring images obtained by capturing an imaging area at different time, a person detection means for detecting a person from each image, an object detection means for detecting an object other than a person from each image, a possession determination means for determining presence of a possession relationship between a person and an object detected from the same image, a same person determination means for determining whether a person detected from one image and a person detected from another image are the same person, a same object determination means for determining whether an object detected from one image and an object detected from another image are the same object, and an event determination means for determining whether a change has occurred in the possession relationship between the person and the object based on the respective determination results, and outputting a determination result.

A device includes an image acquisition means for acquiring images obtained by capturing an imaging area at different time, a person detection means for detecting a person from each image, an object detection means for detecting an object other than a person from each image, a possession determination means for determining presence of a possession relationship between a person and an object detected from the same image, a same person determination means for determining whether a person detected from one image and a person detected from another image are the same person, a same object determination means for determining whether an object detected from one image and an object detected from another image are the same object, and an event determination means for determining whether a change has occurred in the possession relationship between the person and the object based on the respective determination results, and outputting a determination result.

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

A face recognition system, a face recognition method, and a storage medium that can perform face matching smoothly in a short time are provided. The face recognition system includes: a face detection unit that detects a face image from an image including an authentication subject as a detected face image; a storage unit stores identification information identifying the authentication subject and a registered face image of the authentication subject in association with each other; and a face matching unit that, in response to acquisition of the identification information identifying the authentication subject, matches, against the registered face image corresponding to the acquired identification information, the detected face image detected by the face detection unit from an image captured before the acquisition.

Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

Techniques for improving artificial intelligence-based base calling are disclosed. The improved techniques can be used to better train artificial intelligence for base calling by reordering of sequencing images, and training of a neural network-based base caller where the temporal logic is effectively “frozen” (or bypassed). In addition, the improved techniques include various combinations, including, for example, combining “normalization” of sequencing images with reordering of sequencing images and/or with effectively “freezing” the temporal logic.