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.

The disclosure relates to image recognition, in particular it relates to a method for classifying an obscured object, by identifying an object in an image as an obscured object, calculating a 3D space for the image, defining a 3D coordinate for the obscured object, retrieving a plurality of 3D models from a first database, rendering a 2D model of each one of the retrieved 3D models, calculating a similarity score between the rendered 2D representation and the obscured object, and classifying the obscured object as the object of the 3D model for which a highest similarity score was determined. The disclosure further relates to a device and a computer readable program for carrying out such a method.

A method and device of similarity determination, network training, and search, an electronic device, and a storage medium are provided. The data similarity determination method includes: acquiring first data of a first object; mapping the first sub-data as a first semantic representation in a semantic comparison space, where the semantic comparison space enables a similarity between a semantic representation obtained by mapping data of the first modality to the semantic comparison space and a semantic representation obtained by mapping data of the second modality to the semantic comparison space to be computed; acquiring second data of a second object; mapping the second sub-data as a second semantic representation in the semantic comparison space; and calculating a similarity between the first data and the second data based on at least the first semantic representation and the second semantic representation.

Systems and methods for assessing plotting device accuracy in aid of a process for qualifying plotter systems. The system and process involve an ideal (virtual) test pattern consisting of digital data defining a nominal grid augmented by geometric features (e.g., closed two-dimensional geometric shapes which respectively surround intersections or vertices of the nominal grid). The plotting device under test is commanded to print a test plot having a pattern that matches the test pattern, but the test plot may deviate from the test pattern. To measure the amount of deviation, first an image of the test plot on the printed medium is captured using an accurate optical scanner. Then a mathematical measurement process, implemented in a computer vision application (e.g., a software package), is employed to detect deviations of the test plot from the test pattern. A statistical analysis is then performed to determine whether the deviations are within specifications.

Implementations relate to dynamic adaptation of images for projection by a projector, based on one or more properties of user(s) that are in an environment with the projector. The projector can be associated with an automated assistant client of a client device. In some versions of those implementations, a pose of a user in the environment is determined and, based on the pose, a base image for projecting onto a surface is warped to generate a transformed image. The transformed image, when projected onto a surface and viewed from the pose of the user, mitigates perceived differences relative to the base image. The base image (on which the transformed image is based) can optionally be generated in dependence on a distance of the user. Some implementations additionally or alternatively relate to dynamic adaptation of projection parameters (e.g., a location for projection, a size of projection) based on one or more properties of user(s) that are in an environment with the projector.

A system segments a set of images of a property to identify a type of damage to the property. The system receives, from an image capturing device, a digital image of a roof or other feature of the property. The system processes the image to identify a set of segments, in which each segment corresponds to a piece of the feature, such as a tab or tooth of a shingle on the roof. The system saves a result of the processing to a data file as a segmented image of the property, and it uses the segmented image to identify a type of damage to the property.

Implementations relate to dynamic adaptation of images for projection by a projector, based on one or more properties of user(s) that are in an environment with the projector. The projector can be associated with an automated assistant client of a client device. In some versions of those implementations, a pose of a user in the environment is determined and, based on the pose, a base image for projecting onto a surface is warped to generate a transformed image. The transformed image, when projected onto a surface and viewed from the pose of the user, mitigates perceived differences relative to the base image. The base image (on which the transformed image is based) can optionally be generated in dependence on a distance of the user. Some implementations additionally or alternatively relate to dynamic adaptation of projection parameters (e.g., a location for projection, a size of projection) based on one or more properties of user(s) that are in an environment with the projector.

Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.

A system and a method of classifying data and providing an accuracy of classification are described. The method includes determining values of statistical features associated with data packets present in a data stream. The values of statistical features are provided to a data model for producing a classification output including the data packets classified into one or more categories. While producing the classification output, the data model extracts heuristics for each of the values of statistical features, compares the heuristics with one or more conditional checks defined at each node within the data model, and determines a cumulative score based on results of the comparing. The cumulative score is determined by aggregating a score assigned to successful clearance of each conditional check. The cumulative score indicates an accuracy of the classification output.

A method for processing digital image recognition of invariant representations of hierarchically structured entities can be performed by a computer using an artificial neural network. The method involves learning a sparse coding dictionary on an input signal to obtain a representation of low-complexity components. Possible transformations are inferred from the statistics of the sparse representation by computing a correlation matrix. Eigenvectors of the Laplacian operator on the graph whose adjacency matrix is the correlation matrix from the previous step are computed. A coordinate transformation is performed to the base of eigenvectors of the Laplacian operator, and the first step is repeated with the next higher hierarchy level until all hierarchy levels of the invariant representations of the hierarchically structured entities are processed and the neural network is trained. The trained artificial neural network can then be used for digital image recognition of hierarchically structured entities.