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.

Methods and systems are provided for electronic authentication. A modified electronic image is generated by altering at least a pixel of an electronic image. The electronic image is an image that has been previously viewed by a user during a setup process. In response to receiving an authentication request from the user, the modified electronic image is displayed to the user via an electronic display along with one or more other electronic images. A determination is made as to whether the user is able to recognize the modified electronic image. In response to determination that the user is able to recognize the modified electronic image, the authenticating request is granted.

Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.

Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.

A new image-based representation and an associated reference image is disclosed called the emotion avatar image (EAI), and the avatar reference, respectively, which leverages the out-of-plane head rotation. The method is not only robust to outliers but also provides a method to aggregate dynamic information from expressions with various lengths. The approach to facial expression analysis can consist of the following steps: 1) face detection; 2) face registration of video frames with the avatar reference to form the EAI representation; 3) computation of features from EAI using both local binary patterns and local phase quantization; and 4) the classification of the feature as one of the emotion type by using a linear support vector machine classifier.

An image processing apparatus is configured to extract an object region from an image. The image processing apparatus includes: a setting unit configured to set a plurality of reference points in the image; an obtaining unit configured to obtain a contour of the object region corresponding to each of the plurality of reference points as an initial extraction result based on a characteristic of the object region; and an extraction unit configured to extract the object region from the image based on an integration result obtained by integrating values of pixels in a plurality of initial extraction results.

Example image processing methods, apparatus/systems and articles of manufacture are disclosed herein. An example apparatus includes an image recognition application to identify matches between stored patterns and objects detected in a shelf image, where the shelf image has a shelf image scale estimate. The example apparatus further includes a scale corrector to calculate deviation values between sizes of (A) a first set of the objects detected in the shelf image and (B) a first set of the stored patterns matched with the first set of the objects and reduce an error of the shelf image scale estimate by calculating a scale correction value for the shelf image scale estimate based on the deviation values.

A method for enforcing smoothness constraints on surface meshes produced by a Graph Convolutional Neural Network (GCNN) including the steps of reading image data from a memory, the image data including two-dimensional image data representing a three-dimensional object or a three-dimensional image stack of the three-dimensional object, performing a GCNN mesh deformation step on the image data to obtain an approximation of a surface of the three-dimensional object, the surface represented by triangulated surface meshes, at least some vertices of the triangulated surface meshes having a different number of neighboring vertices compared to other vertices in a same triangulated surface mesh, and performing a deep active surface model (DASM) transformation step on the triangulated surface meshes to obtain a corrected representation of the surface of three-dimensional object to improve smoothness of the surface.

A method for detecting defects in working CNC tools in real time, implemented in an electronic device, includes acquiring sounds of operation of a tool during a cutting or other operation process and dividing the acquired cutting sounds into a plurality of recordings of audio according to a preset time interval. Time-frequency features of the plurality of recordings of audio are acquired according to multiple feature transformation methods and a fusion feature image of the cutting sound is formed according to the extracted time-frequency features. A tool detection model is generated by training the fusion feature image, and any defects of the tool and any defect types the tool has are detected according to the tool detection model.

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.