An optical system for capturing an image using compressive sensing includes: a digital micromirror device (DMD) array; an optical lens system; a first optical detector array; a first optical channel for projecting spatial information onto the first detector array; a second optical detector array; a second optical channel; a spectral filter and a polarization filter for projecting spectral and polarization information onto the second detector array; and an image processor to control the DMD array to generate a first and a second set of samples of the image using a sampling rate lower than required by the Shannon-Nyquist sampling theorem, and to reconstruct the image from the samples collected and digitized by the first and second optical detector arrays.

A method for authenticating an object, comprising determining a physical dispersion pattern of a set of elements, determining a physical characteristic of the set of elements which is distinct from a physical characteristic producible by a transfer printing technology, determining a digital code associated with the object defining the physical dispersion pattern, and authenticating the object by verifying a correspondence of the digital code with the physical dispersion pattern, and verifying the physical characteristic.

Systems and methods are provided for enhancing object feature visibility for overhead imaging. In one embodiment, a computing system can obtain information associated with one or more locations of an imaging platform and one or more locations of a solar source. The system can determine one or more positional ranges of the imaging platform relative to the solar source based, at least in part, on such information. The positional ranges can be indicative of positions at which the imaging platform is to obtain image frames depicting at least a portion of a target object. The system can send, to the imaging platform, a set of data indicative of the positional ranges and can receive, from the imaging platform, a set of data indicative of the image frames depicting at least a portion of the target object. The image frames being captured based, at least in part, on the positional ranges.

A system for performing automatic cinemagraph creation is described herein. The system comprises a memory and a processor. The memory is configured to receive series of images. The processor is coupled to the memory. The processor is to segment the series of images, select the most fitting times and mask, and apply the times and masks to the series of images to generate a cinemagraph.

A device configured to emulate a distance measuring device in a correlithm object processing system. The device is configured to receive an input binary string and to mask a first portion of the input binary string. The device is configured to identify a first binary string in a counting table matching a second portion of the input binary string, to fetch a first numeric value linked with the first binary string, and to increment a count value of a counter by the first numeric value. The device is configured to mask the second portion and to unmask the first portion. The device is configured to identify a second binary string in the counting table matching the first portion, to fetch a second numeric value linked with the second binary string, to increment the count value of the counter by the second numeric value, and to output the count value.

A device configured to emulate an image output adapter for a correlithm object processing system that includes an actor engine. The actor engine is configured to receive an aggregated correlithm object corresponding with a mask and to identify a plurality of correlithm objects in the aggregated correlithm object. Each mask table is linked with a mask that defines an array of pixels of an image. The actor engine is configured to populate each pixel location in the mask with a correlithm object from the plurality of correlithm object in accordance with a mask table for the mask. The actor engine is further configured to determine a pixel value for each pixel location in the mask based on the correlithm object at each pixel location and to output a representation of a portion of the image based on the mask populated with pixel values at each pixel location.

A system for detection and classification of findings in an image, comprising at least one hardware processor configured to: receive the image; process the image by a plurality of convolutional and pooling layers of a neural network to produce a plurality of feature maps; process one of the feature maps by some of the layers and another plurality of layers to produce a plurality of region proposals; produce a plurality of region of interest (ROI) pools by using a plurality of pooling layers to downsample the plurality of region proposals with each one of the plurality of feature maps; process the plurality of ROI pools by at least one concatenation layer to produce a combined ROI pool; process the combined ROI pool by a classification network comprising some other of the convolutional and pooling layers to produce one or more classifications; and output the one or more classifications.

An image processing apparatus includes a first phase difference detector configured to detect two phase differences in a range that contains a phase difference that provides the highest correlation between a pair of image signals, a comparator configured to compare a signal representative of a matching degree when the pair of image signals have a first phase difference among the two phase differences, and a signal representative of a matching degree when the pair of image signals have a second phase difference among the two phase differences, a signal separator configured to separate a pair of signal components relating to a specific object from the pair of image signals, based on a comparison result by the comparator, and a second phase difference detector configured to detect a phase difference that provides the highest correlation between the pair of signal components separated by the signal separator.

A computing system includes a processing system with at least one processing unit. The processing system is configured to execute a face alignment method upon receiving image data with a facial image. The processing system is configured to apply a neural network to the facial image. The neural network is configured to provide a final estimate of parameter data for the facial image based on the image data and an initial estimate of the parameter data. The neural network includes at least one visualization layer, which is configured to generate a feature map based on a current estimate of the parameter data. The parameter data includes head pose data and face shape data.

Systems, methods, and computer readable media to categorize a pixel (or other element) in an image into one of a number of different categories are described. In general, techniques are disclosed for using properties (e.g., statistics) of the regions being categorized to determine the appropriate size of window around a target pixel (element) and, when necessary, the manner in which the window may be changed if the current size is inappropriate. More particularly, adaptive window size selection techniques are disclosed for use when categorizing an image's pixels into one of two categories (e.g., black or white). Statistics of the selected region may be cascaded to determine whether the current evaluation window is acceptable and, if it is not, an appropriate factor by which to change the currently selected window's size.