This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

Methods and apparatuses are described for automated cross-browser user interface testing. A computing device captures (i) a first image file corresponding to a first current user interface view of a web application on a first testing platform and (ii) a second image file corresponding to a second current user interface view of a web application on a second testing platform. The computing device prepares the image files, and compares the prepared image files using a structural similarity index measure. The computing device determines that the prepared first image file and the prepared second image file represent a common user interface view when the structural similarity index measure is within a predetermined range. The computing device highlights corresponding regions that visually diverge from each other in each of the prepared image files and transmits a notification message comprising the highlighted image files.

An image processing method and apparatus, a device, and a storage medium are provided. The image processing method includes: obtaining key points of a reference region of an object in an image; determining the orientation of the reference region according to the key points of the reference region; and performing deformation processing on a region to be adjusted of the object based on the orientation of the reference region, where the region to be adjusted is the same as or different from the reference region.

A method for dynamically measuring deformation of a rotating-body mold, including: (S1) subjecting an overall outer surface of the rotating-body mold to three-dimensional measurement to acquire an initial point cloud data; (S2) shooting, by a multi-camera system, the mold from different angles to obtain three-dimensional coordinates of marking points and coding points on the overall outer surface of the rotating-body mold; (S3) rotating the mold, and repeatedly photographing the marking points and the coding points on the mold surface under different angle poses; and calculating three-dimensional coordinates of the marking points and the coding points; and (S4) predicting a point cloud data of the outer surface under different angle poses based on a conversion relationship among the marking points to analyze a deformation degree of the mold during a rotation process.

Systems, computer program products, and methods are described herein for real-time imitation network generation using artificial intelligence. The present invention is configured to electronically receive, from a computing device of a user, a real dataset; initiate one or more machine learning algorithms on the real dataset; determine, using the one or more machine learning algorithms, one or more data distribution parameters associated with the real dataset; electronically receive, from the computing device of the user, a first shift parameter; skew the one or more data distribution parameters using the first shift parameter to generate one or more skewed data distribution parameters; and generate, using the one or more machine learning algorithms, an imitation dataset using the one or more skewed data distribution parameters.

In order to ensure that similarity is detected with high accuracy, regardless of the types of images constituting a group of images to be evaluated for similarity, the image processing device includes difference calculation means 11 for deforming one of two or more images constituting an image group in one or more deforming ways, and calculating a degree of difference between the deformed image and the other image or images in the image group for each pixel using multiple ways for similarity evaluation, normalization means 12 for normalizing each degree of difference by each of the multiple ways for similarity evaluation, and difference integration means 15 for integrating normalized degrees of difference.

Disclosed is a computer-implemented method for determining the pose of an anatomical body part of a patient's body for planning radiation treatment, a corresponding computer program, a non-transitory program storage medium storing such a program and a computer for executing the program, as well as a system for determining the pose of an anatomical body part of a patient's body for planning radiation treatment, the system comprising an electronic data storage device and acquire surface tracking data the aforementioned computer.

The invention notably relates to a computer-implemented method comprising providing (S10) a geomodel representing horizons and geological units of the reservoir; providing (S20), for each well a trajectory representing the path of the well in the reservoir, and data distributed along the trajectory. The data include horizon markers, one or more fault markers, and a geological unit log. The method comprises deforming (S30) the trajectory of at least one well based on the geomodel. The deforming is constrained by consistency of the horizon markers and of the geological unit log with the geomodel, a discontinuity being allowed at each fault marker. This provides an improved solution for obtaining consistency between the geomodel and the trajectory of the at least one well.

Various examples are provided for obtaining body measurements of a user, predicting body measurements of a user, and/or the like. Examples include a coded dimensioning garment having a stretchable fabric that conforms to a wearer's body without substantial compression or distortion of the skin. The garment can include measurement markings of known sizes at one or more known locations on the fabric. Methods are provided for predicting body measurements from a user. Image(s) of a user wearing the garment in a reference pose are captured and can be uploaded to a body measurement application. The real-world dimensions can be converted to pixel dimensions in the same ratio as the real world dimensions and inputted into a prediction model to generate measurements from areas of interest on the user.

Signal processing devices and methods estimate transforms between signals using a least squares technique. From a seed set of transform candidates, a direct least squares method applies a seed transform candidate to a reference signal and then measures correlation between the transformed reference signal and a suspect signal. For each candidate, update coordinates of reference signal features are identified in the suspect signal and provided as input to a least squares method to compute an update to the transform candidate. The method iterates so long as the update of the transform provides a better correlation. At the end of the process, the method identifies a transform or set of top transforms based on a further analysis of correlation, as well as other results.