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.

An image identification method is provided, including: storing at least one normal state image of at least one test object; an automatic codec receiving the at least one normal state image to become a trained automatic codec; at least one camera device capturing at least one state image of the at least one test object; a computer device receiving the at least one state image, and the trained automatic codec performing feature extraction and reconstruction on the at least one state image to generate at least one reconstructed state image; and the computer device comparing the at least one state image and the at least one reconstructed state image, and determining whether the at least one state image is a normal state image. The present invention also provides an image identification system.

A method for adapting a matching model of an object comprising the step of providing an electronic image of the object; providing a matching model of the object, the matching model consisting of a plurality of points; determining a pose of the object in said electronic image by using a matching approach that uses said matching model; transforming the matching model according to said pose, yielding a transformed model; determining for at least one point of said transformed model a corresponding point in said electronic image; and adapting the matching model according to the at least one determined corresponding point.

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.

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.

Provided is a 3D registration method and apparatus that may select a key point from among plural points included in 3D target data, based on a geometric feature or a color feature of each of the plural points, may adjust a position of the selected key point of the 3D target data based on features of, or a distance between, a key point of the 3D source data and the selected key point of the 3D target data, may calculate reliabilities of plural key points of the 3D source data based on respective features of at least one key point of the 3D target data determined to correspond to the plural key points of the 3D source data, and may generate 3D registration data by performing 3D registration between the 3D source data and the 3D target data based on the calculated reliabilities.

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.

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.

An image processing apparatus includes: an obtaining unit configured to obtain corresponding points group information for associating a position in an image captured in a first deformation state with a position in an image captured in a second deformation state; a calculation unit configured to calculate deformation from the image captured in the first deformation state to the image captured in the second deformation state based on the corresponding points group information; a display control unit configured to display, on a display unit, deformation progress information of an image based on the deformation calculated by the calculation unit; and a correction unit configured to correct the corresponding points group information. The calculation unit calculates the deformation based on the corresponding points group information corrected by the correction unit.

Treatment trajectory guidance systems and methods are provided. In one embodiment, the method for treatment trajectory guidance in a patient's brain includes obtaining a three- dimensional (3D) brain model that includes a model of an anatomy, the model of the anatomy including a plurality of feature points; modifying the 3D brain model based on magnetic resonance (MR) data of the patient's brain from a magnetic resonance imaging (MRI) device to obtain a plurality of modified feature points on a modified model of the patient's anatomy in the patient's brain; displaying on a display a first planned trajectory for treating the patient's anatomy based on the plurality of modified feature points; and displaying, on the display, a first estimated treatment result for the first planned trajectory.