Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for simulating, predicting, or estimating, based on machine learning neural networks, wall stress of a body part. In one approach, a first neural network automatically detects features in multiple images of a body part. For example, the first neural network may detect, for each image, a lumen and a wall of an aorta. According to the detected features, a second neural network may simulate, estimate, or predict wall stress of the body part in response to pressure applied to the body part. For example, a model generator can generate a three-dimensional model of the body part according to the detected features in the multiple images, and the second neural network can simulate, estimate, or predict wall stress of the body part according to the three-dimensional model.

An example system includes a stand configured to position a computing device proximate to a physical activity surface. The system further includes a video capture device, a detector, and an activity application. The video capture device is coupled for communication with the computing device and is adapted to capture a video stream that includes an activity scene of the physical activity surface and one or more interface objects physically interactable with by a user. The detector is executable to detect motion in the activity scene based on the processing and, responsive to detecting the motion, process the video stream to detect one or more interface objects included in the activity scene of the physical activity surface. The activity application is executable to present virtual information on a display of the computing device based on the one or more detected interface objects.

Systems and methods are disclosed for recommending products or services by receiving a three-dimensional (3D) model of one or more products; performing motion tracking and understanding an environment with points or planes using accelerometer sensor and estimating light or color in the environment using one video camera without a depth sensor in a mobile phone; acquiring sensor data from sensors and optimizing features extracted from each image and sensor data, where a feature conveys data unique to the image at a specific pixel location; and projecting the product in the environment.

A global registration system and method identifies bronchoscope position without the need for significant bronchoscope maneuvers, technician intervention, or electromagnetic sensors. Virtual bronchoscopy (VB) renderings of a 3D airway tree are obtained including VB views of branch positions within the airway tree. At least one real bronchoscopic (RB) video frame is received from a bronchoscope inserted into the airway tree. An algorithm according to the invention is executed on a computer to identify the several most likely branch positions having a VB view closest to the received RB view, and the 3D position of the bronchoscope within the airway tree is determined in accordance with the branch position identified in the VB view. The preferred embodiment involves a fast local registration search over all the branches in a global airway-bifurcation search space, with the weighted normalized sum of squares distance metric used for finding the best match.

A system for detecting defects in a print system may include a print engine to print an electronic document (a reference image) and yield a printed document. The system may also include an image sensor to scan the printed document into a scanned electronic document (a target image). The system may include a processing device to detect defects in the printed document by analyzing the activity level in the reference and target image. The system may identify a quiet area/pixel in the reference image based on the activity level, check the activity level of the corresponding pixel in the target image, and classify the pixel in the target image as defective if the activity level of the pixel in the target image exceeds a noise threshold. The system may additionally swap the reference and target image, repeat the detection steps and combine the detection results with those before the swap.

An image system for detecting defects in an image may include a processing device to detect defects in a target image by analyzing the activity level in a reference and the target image. The system may identify a quiet area/pixel in the reference image based on the activity level, check the activity level of the corresponding pixel in the target image, and classify the pixel in the target image as defective if the activity level of the pixel in the target image exceeds a noise threshold. The system may additionally swap the reference and target image, repeat the detection steps and combine the detection results with those before the swap. The system may also include an image sensor to scan a printed document of the reference image into a scanned electronic document (the target image).

In some implementations, a computer-implemented method for recognizing facial images may include a multi-stage facial verification process to improve the speed and accuracy of a facial recognition operation. For example, a facial recognition module may include multiple stages where a subset of data is analyzed recursively to improve the speed of the facial recognition processes. The multiple stages may be arranged in a fast-to-slow and coarse-to-fine arrangements such that a match decision may be made at each successive stage.

Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

The present disclosure discloses a method and apparatus for human face image processing. A specific embodiment of the method comprises: locating facial feature points in a human face image, extracting an image of a human face region according to a range defined by the facial feature points, transforming a facial image of the source image according to a face shape of the target image, transforming the facial image of the source image according to a complexion distribution of a facial region of the target image, and obtaining a new human face by combining the facial image of the source image and a facial image of the target image. The embodiment achieves a facial image processing with higher similarity to the user in the image, of simple steps, small calculation and high real-time performance.

Provided is a system and method for estimating the motion of a target inside a tissue based on surface deformation of the soft tissue. The system consists of an acquisition unit, a reference input unit, two surface extraction units, a target position extraction unit, a feature calculation unit, and a target motion estimation unit. The method includes: the acquisition unit acquires an image I.sub.i of the soft tissue; the surface extraction unit extracts a surface f.sub.i of the soft tissue from I.sub.i; the reference input unit acquires a reference image I.sub.ref of the soft tissue; the surface extraction unit and the target position extraction unit respectively extract a reference surface f.sub.ref of the soft tissue and a target reference position t.sub.ref from I.sub.ref, the feature calculation unit calculates deformation feature .sub.i of f.sub.i relative to f.sub.ref, the target motion estimation unit estimates the target displacement based on .sub.i and t.sub.ref.