The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

Systems and methods are described for performing dynamic time warping using diffusion wavelets. Embodiments of the inventive concept integrate dynamic time warping with multi-scale manifold learning methods. Certain embodiments also include warping on mixed manifolds (WAMM) and curve wrapping. The described techniques enable an improved data analytics application to align high dimensional ordered sequences such as time-series data. In one example, a first embedding of a first ordered sequence of data and a second embedding of a second ordered sequence of data may be computed based on generated diffusion wavelet basis vectors. Alignment data may then be generated for the first ordered sequence of data and the second ordered sequence of data by performing dynamic time warping.

Techniques for recommending a prediction model from among a number of different prediction models are provided. Each of these prediction models has been trained based on a respective training data set, and each performs in accordance with a respective theoretical performance manifold. An indication of a region definable in relation to the theoretical performance manifolds of the different prediction models is received as input. For each of the different prediction models, the indication of the region is linked to features parameterizing the respective performance manifold; and one or more portions of the respective performance manifold is/are identified based on the features determined by the linking, the portion(s) having a volume and a shape that collectively denote an expected performance of the respective model for the input. The expected performance of the prediction models for the input is compared. Based on the comparison, one or more of the models is/are suggested.

Embodiments of the present disclosure disclose an anomaly detector for detecting an anomaly in a sequence of poses of a human performing an activity. The anomaly detector includes an input interface configured to accept input data indicative of a distribution of the sequence of poses, a memory configured to store a discriminative one-class classifier having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space (RKHS), a processor configured to embed the input data into an element of the RKHS and classify the embedded data using the discriminative one-class classifier, and an output interface configured to render a classification result.

Systems and methods for authenticating dendritic product tags are disclosed. An authentication authority fabricates and digitally images a dendrite. A shape of the dendrite is numerically modeled as a graph including nodes. The nodes correspond to seed, bifurcation and termination points of the dendrite. Each node is associated in a database with a two value vector corresponding to the length and orientation of a linear approximation of the branch terminating at the node. This model is compared to a model built by a remote application of a dendritic tag encountered in the field, and product information including an indication of authenticity is sent if the models match. Matching occurs by an ad-hoc comparison between nodes in the models, which comparison involves comparing child, parent and sibling nodes.

Systems and methods are provided for generating a rear view image display for a motor vehicle. A rear view system includes an optical sensor disposed at the motor vehicle and configured to capture image data, a computational unit coupled to the optical sensor by a cable connection and configured to execute program instructions stored on a computer-readable medium to modify the image data for presentation, and a display device coupled to the computational unit and configured to receive the modified image data from the computational unit and display the modified image data to a driver of the motor vehicle. The computational unit is further configured to receive software calibration to optimize modification of the image data.

Disclosed are computer-implemented methods, non-transitory computer-readable media, and systems for authentication. One computer-implemented method includes obtaining a first image, where the first image is an image of an identification (ID) document captured when the ID document is tilted at a first angle relative to a projected light source. A second image is obtained, where the second image is an image of the ID document captured when the ID document is tilted at a second angle relative to the projected light source. The ID document is authenticated based on identification of a first illuminated region and a second illuminated region, where the first illuminated region is associated with the first image and the second illuminated region is associated with the second image, and a comparison between a first position of the first illuminated region and a second position of the second illuminated region.

A multi-sensor threat detection system and method for elevated temperature detection using commodity-based thermal cameras and mask wearing compliance using optical cameras. The proposed method does not rely on the accuracy of thermal cameras, but the combination of mathematics, statistics, machine learning, artificial intelligence, computer vision and Manifold learning to construct a classifier, or set of classifiers, that are able to, either alone or working as an ensemble, evaluate a person as being ‘normal temperature’ or ‘elevated temperature’ by virtue of ‘how they present to the camera’ vs. any absolute temperature measurements from the camera itself.

A retina image template matching method is based on the registration and comparison between the images captured with portable low-cost fundus cameras (e.g., a consumer grade camera typically incorporated into a smartphone or tablet computer) and a baseline image. The method solves the challenges posed by registering small and low-quality retinal template images captured with such cameras. Our method combines dimension reduction methods with a mutual information (MI) based image registration technique. In particular, principle components analysis (PCA) and optionally block PCA are used as a dimension reduction method to localize the template image coarsely to the baseline image, then the resulting displacement parameters are used to initialize the MI metric optimization for registration of the template image with the closest region of the baseline image.

Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.