A content device and method is disclosed to include a processing device to process streaming video content. A fingerprinter receives captured frames of the streaming video content and, for each frame of a plurality of the captured frames, generates a one-dimensional histogram function of pixel values and transforms the histogram function with a Fast Fourier Transform (FFT), to generate a plurality of complex values for the frame. The fingerprinter further, for each of the plurality of complex values, assigns a binary one (“1”) when a real part of the complex value is greater than zero (“0”) and assigns a binary zero (“0”) when the real part is less than or equal to zero, to generate a plurality of bits. The fingerprinter further concatenates a specific number of the bits to generate a fingerprint for the frame.

An input unit (20) obtains a sequence of image frames over time. A segmentation unit (22) segments image frames of the sequence of image frames. A tracking unit (24) tracks segments of the segmented image frame over time in the sequence of image frames. A clustering unit (26) clusters the tracked segments to obtain clusters representing skin of a subject by use of one or more image features of the tracked segments.

A method comprising: receiving an image stream of a mammalian cardiac muscle cell; defining or having defined a region of interest (ROI) within said cell; calculating a fast Fourier transform (FFT) of said ROI; detecting peaks in said FFT; and determining at least one of sarcomere length, mitochondrial length, and mitochondrial width, based, at least in part, in said detected peaks.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

A pattern recognition system including an image gathering unit that gathers at least one digital representation of a field, an image analysis unit that pre-processes the at least one digital representation of a field, an annotation unit that provides a visualization of at least one channel for each of the at least one digital representation of the field, where the image analysis unit generates a plurality of image samples from each of the at least one digital representation of the field, and the image analysis unit splits each of the image samples into a plurality of categories.

A computer that identifies a fake image is described. During operation, the computer receives an image. Then, the computer performs analysis on the image to determine a signature that includes multiple features. Based at least in part in the determined signature, the computer classifies the image as having a first signature associated with the fake image or as having a second signature associated with a real image, where the first signature corresponds to a finite resolution of a neural network that generated the fake image, a finite number of parameters in the neural network that generated the fake image, or both. For example, the finite resolution may correspond to floating point operations in the neural network. Moreover, in response to the classification, the computer may perform a remedial action, such as providing a warning or a recommendation, or performing filtering.

A system and method for detection of synthesized videos of humans. The method including: determining blood flow signals using a first machine learning model trained with a hemoglobin concentration (HC) changes training set, the first machine learning model taking as input bit values from a set of bitplanes in a captured image sequence, the HC changes training set including bit values from each bitplane of images captured from a set of subjects for which HC changes are known; determining whether blood flow patterns from the video are indicative of a synthesized video using a second machine learning model, the second machine learning model taking as input the blood flow signals, the second machine learning model trained using a blood flow training set including blood flow data signals from at least one of a plurality of videos of other human subjects for which it is known whether each video is synthesized.

A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Computational techniques are applied to video images of polyps to extract features and patterns from different perspectives of a polyp. The extracted features and patterns are synthesized using registration techniques to remove artifacts and noise, thereby generating improved images for the polyp. The generated images of each polyp can be used for training and testing purposes, where a machine learning system separates two types of polyps.

The present invention provides an unsupervised domain adaptive segmentation network comprises a feature extractor configured for extracting features from a 3D MRI scan image; a decorrelation and whitening module configured for preforming decorrelation and whitening transformation on the extracted features to obtain whitened features; a domain-specific feature translation module configured for translating domain-specific features from a source domain into a target domain for adapting the unsupervised domain adaptive network to the target domain; and a classifier configured for projecting the whitened features into a zonal segmentation prediction. By implementing the domain-specific feature translation module for transferring the knowledge learned from the labeled source domain data to unlabeled target domain data, domain gap between the source and target data can be narrowed. Therefore, the unsupervised domain adaptive segmentation network trained with labeled open-source prostate zonal segmentation dataset (source data) can perform in the target domain without performance degradation.