A method includes receiving data corresponding to a sequence of deformations of a surface of an object. The method also includes generating spherical harmonics expressions based on the data. The method includes identifying a subspace of the spherical harmonics expression corresponding to the surface.

Systems, methods and computer program products for determining perceptual similarity between independent images are described. The described systems, methods and computer program products use a similarity distance to determine the perceptual similarity between a first image having a first arrangement of pixels and a second image having a second arrangement of pixels. A processor can determine a first image array corresponding to the first arrangement of pixels and a second image array corresponding of the second arrangement of pixels. The processor can generate a first pixel group set based on the first image array and a second pixel group set based on the second image array. A plurality of pixel group pairs can be generated, and each pixel group pair can be associated with a corresponding pixel group pair distortion. The similarity distance between the first image and the second image can be determined based on the plurality of pixel group pair distortions.

The subject matter described herein relates to face alignment via shape regression. A method, computer storage medium, and system are provided. In one embodiment, the method comprises receiving an image including a face; and performing shape regression to estimate a facial shape in the image. For each stage in the shape regression, a local feature is extracted from a local region around each facial landmark in the image independently; and a joint projection is performed based on local features of multiple facial landmarks to predict a facial shape increment. Then, a facial shape of a current stage is generated based on the predicted facial shape increment and a facial shape of a previous stage.

A system for detecting a lane line of a road and a method thereof is disclosed. An image-retrieving device retrieves an image in front of a vehicle to generate a picture, and transmits the picture to a processing device. The processing device transforms one or two lane lines on the picture into a plurality of characteristic values, processes the characteristic values to generate one or two prediction lane lines, and uses them to dynamically predict and complete the lane lines, thereby forming a lane line prediction model. The processing device uses the lane line prediction model to determine whether the vehicle deviates, and transmits the picture and the lane line prediction model to a display device to display. When the vehicle deviates, the display device warns. The present invention can improve to recognize an unclear lane line, a single lane line and a far lane line.

Systems and methods for registering documents. A two-dimensional document image and one or more form images may be obtained. The document image may be projected onto a horizontal axis and a vertical axis to create a horizontal document projection and a vertical document projection. The form images may be projected onto the horizontal axis and the vertical axis to create a horizontal form projection and a vertical form projection. The horizontal document projection may be correlated with the horizontal form projection of the form images and the vertical document projection may be correlated with the vertical form projection of the form images. Correlation scores may be calculated based on the correlations.

A method is disclosed of recognizing a logo of a vehicle. The method including obtaining a limited number of high resolution logos; populating a training dataset for each of the limited number of high resolution logos using gamma transformations; obtaining a low resolution image of a vehicle; and matching the low resolution image of the vehicle with the training dataset.

Systems and methods are disclosed for recommending products or services by receiving a 3D model of a product; capturing a reference object with a predetermined dimension in an environment where the product is to be placed using a mobile camera; determining one more dimensions of the environment relative to the predetermined dimension of the reference object; scaling the 3D model of the product based on dimensions of the environment and the product; and generating an augmented or virtual reality display of the product in the environment.

An ultrasonic diagnostic apparatus that extracts a feature to be satisfied as a measurement cross section, classifies the extracted feature according to the degree of importance, and displays and selects an adequate cross-sectional image for each measurement item is provided. The ultrasonic diagnostic apparatus includes an image processing unit 1005 for processing an RF signal from the ultrasonic diagnostic apparatus to generate a cross-sectional image, and a adequacy determination unit 1007 for determining whether the cross-sectional image is adequate, or not, as a measurement image used for measuring a subject included the cross-sectional image acquired by the image processing unit 1005, and has a configuration in which a result determined by the adequacy determination unit 1007 is displayed on a monitor 1006, and presented to an operator.

Systems and methods are provided for constructing a statistical shape model from a set of training shapes. In one embodiment, two shapes in the training set can be parameterized to a common base domain. Correspondence between the shapes can be evaluated using shape-specific data, such as, for the case of anatomical shapes, anatomical curves and/or anatomical landmarks. In evaluating correspondence, the shape-specific data about the second shape can be mapped to the shape-specific data about the first shape, and the mapping can be optimized based at least in part on a deformation energy of the mapping.

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.