System and method for detecting the authenticity of products by detecting a unique chaotic signature. Photos of the products are taken at the plant and stored in a database/server. The server processes the images to detect for each authentic product a unique authentic signature which is the result of a manufacturing process, a process of nature etc. To detect whether the product is genuine or not at the store, the user/buyer may take a picture of the product and send it to the server (e.g. using an app installed on a portable device or the like). Upon receipt of the photo, the server may process the receive image in search for a pre-detected and/or pre-stored chaotic signature associated with an authentic product. The server may return a response to the user indicating the result of the search. A feedback mechanism may be included to guide the user to take a picture at a specific location of the product where the chaotic signature may exist.

This disclosure relates generally to estimating age of a leaf using morphological features extracted from segmented leaves. Traditionally, leaf age estimation requires a single leaf to be plucked from the plant and its image to be captured in a controlled environment. The method and system of the present disclosure obviates these needs and enables obtaining one or more full leaves from images captured in an uncontrolled environment. The method comprises segmenting the image to identify veins of the leaves that further enable obtaining the full leaves. The obtained leaves further enable identifying an associated plant species. The method also discloses some morphological features which are fed to a pre-trained multivariable linear regression model to estimate age of every leaf. The estimated leaf age finds application in estimation of multiple plant characteristics like photosynthetic rate, transpiration, nitrogen content and health of the plants.

Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Automated cerebral microbleed detection is performed in extracted T2*-weighted image data, including gradient echo (GRE) image data and susceptibility-weighted imaging (SWI) image data. The image data is resampled and potential 2D regions of interest (ROI) having a circular or ellipsoidal shape are identified based in part on a respective intensity of associated resampled image pixels. The number of 2D ROIs are reduced by size, edge, and/or cerebrospinal fluid (CSF) mask exclusion, and then merged to form 3D ROIs. False positive 3D ROIs are removed and the remaining ROIs stored for review by a trained rater. The embodiments of the present disclosure outperform visual ratings of cerebral microbleeds, reducing the time to visually rate the scans while retaining sensitivity to the microbleeds themselves. These embodiments also exhibit higher sensitivity in longitudinal identification of microbleed locations, and are suited to longitudinal examination of cerebrovascular disease, e.g., Alzheimer’s in adults with Down syndrome.

The disclosure relates to methods and systems for detecting the orientation of an oocyte and its polar body. Examples include an automated method for detection of an orientation of an oocyte, the method including: i) acquiring an image of the oocyte; ii) defining first and second elliptical features in the image based on edges detected in the image; iii) calculating an orientation of the second elliptical feature relative to the first elliptical feature; and iv) outputting a relative orientation of a polar body of the oocyte based on the orientation of the second elliptical feature relative to the first elliptical feature.

The present disclosure describes ultrasound systems and methods configured to determine stiffness levels of anisotropic tissue. Systems can include an ultrasound transducer configured to acquire echoes responsive to ultrasound pulses transmitted toward anisotropic tissue having an angular orientation with respect to a nominal axial direction of the transducer. Systems can also include a beamformer configured to control the transducer to transmit a push pulse along a steering angle for generating a shear wave in the anisotropic tissue. The steering angle can be based on the angular orientation of the tissue. The transducer can also be controlled to transmit tracking pulses. Systems can also include a processor configured to store tracking line echo data generated from echo signals received at the transducer. In response to the echo data, the processor can detect motion within the tissue caused by propagation of the shear wave and measure the velocity of the shear wave.

An image processing apparatus having a reading device to read an image from a sheet includes one or more controllers to perform operations. One image is read from one sheet by using the reading device. Edge detection processing is executed on the one image. At least one pair of edges is determined from among a plurality of edges based on information of the plurality of edges detected in the edge detection processing and size information of the one sheet.

A computer-implemented method for tracking surgical textiles includes receiving a first image comprising a first textile-depicting image region, receiving a second image comprising a second textile-depicting image region, measuring a likelihood that the first and second image regions depict at least a portion of the same textile, and incrementing an index counter if the measure of likelihood does not meet a predetermined threshold. The measure of likelihood may be based on at least one classification feature at least partially based on aspects or other features of the first and second images.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a critical edge detection neural network and a geometric model to determine camera parameters from a single digital image. In particular, in one or more embodiments, the disclosed systems can train and utilize a critical edge detection neural network to generate a vanishing edge map indicating vanishing lines from the digital image. The system can then utilize the vanishing edge map to more accurately and efficiently determine camera parameters by applying a geometric model to the vanishing edge map. Further, the system can generate ground truth vanishing line data from a set of training digital images for training the critical edge detection neural network.